Whole Brain Emulation
A Roadmap(2008) Technical Report #2008‐3
Anders Sandberg*
Nick Bostrom
Future of Humanity Institute
Faculty of Philosophy & James Martin 21st Century School
Oxford University
CITE: Sandberg, A. & Bostrom, N. (2008): Whole Brain Emulation: A Roadmap, Technical Report #2008‐3, Future of
Humanity Institute, Oxford University
URL: www.fhi.ox.ac.uk/reports/2008‐3.pdf(*) Corresponding author: anders.sandberg@philosophy.ox.ac.uk

In memoriam: Bruce H. McCormick (1930 – 2007)

2

Contents
Whole Brain Emulation............................................................................................................................1
A Roadmap ................................................................................................................................................1
In memoriam: Bruce H. McCormick (1930 – 2007)...........................................................................2
Contents..................................................................................................................................................3
Introduction ...........................................................................................................................................5
Thanks to ............................................................................................................................................6
The concept of brain emulation..........................................................................................................7
Emulation and simulation...............................................................................................................7
Little need for whole‐system understanding...............................................................................8
Levels of emulation and success criteria.....................................................................................10
Scale separation...............................................................................................................................12
Simulation scales.............................................................................................................................13
WBE assumptions ...........................................................................................................................15
Roadmap ..............................................................................................................................................16
Requirements...................................................................................................................................16
Linkages............................................................................................................................................19
Roadmap ..........................................................................................................................................20
Technology drivers.........................................................................................................................23
Uncertainties and alternatives......................................................................................................24
Alternative pathways.....................................................................................................................27
Related technologies and spin‐offs ..............................................................................................28
Issues.....................................................................................................................................................30
Emulation systems..........................................................................................................................30
Complications and exotica ............................................................................................................31
Summary ..........................................................................................................................................39
Scanning ...............................................................................................................................................40
Embedding, fixation and staining techniques ...........................................................................52
Conclusion .......................................................................................................................................53
Image processing and scan interpretation......................................................................................55
Geometric adjustment....................................................................................................................55
Noise removal .................................................................................................................................56
Data interpolation...........................................................................................................................56
Cell tracing .......................................................................................................................................57
Synapse identification....................................................................................................................59
Identification of cell types .............................................................................................................60
Estimation of parameters for emulation .....................................................................................61
Connectivity identification............................................................................................................62
Conclusion .......................................................................................................................................63
Neural simulation ...............................................................................................................................64
How much neuron detail is needed?...........................................................................................64
Neural models .................................................................................................................................66
Simulators ........................................................................................................................................70
Parallel simulation..........................................................................................................................70
Current large‐scale simulations....................................................................................................71
Conclusion .......................................................................................................................................72
Body simulation ..................................................................................................................................74
Conclusion .......................................................................................................................................75
Environment simulation....................................................................................................................763

Vision ................................................................................................................................................76
Hearing .............................................................................................................................................77
Smell and Taste ...............................................................................................................................77
Haptics..............................................................................................................................................77
Conclusion .......................................................................................................................................78
Computer requirements ....................................................................................................................79
Conclusions......................................................................................................................................81
Validation.............................................................................................................................................82
Discussion ............................................................................................................................................83
Appendix A: Estimates of the computational capacity/demands of the human brain ..........84
Appendix B: Computer Performance Development ....................................................................86
Processing Power............................................................................................................................86
Memory ............................................................................................................................................95
Disc drives........................................................................................................................................97
Future................................................................................................................................................98
Appendix C: Large‐scale neural network simulations...............................................................101
Appendix D: History and previous work.....................................................................................105
Appendix E: Non‐destructive and gradual replacement...........................................................107
Non‐Destructive Scanning ..........................................................................................................107
Gradual replacement....................................................................................................................108
Appendix F: Glossary.......................................................................................................................110
References ..........................................................................................................................................113

4

Introduction
Whole brain emulation (WBE), the possible future one‐to‐one modelling of the function of thehuman brain, is academically interesting and important for several reasons:
•

•

•

•

Researcho Brain emulation is the logical endpoint of computational neuroscience’s
attempts to accurately model neurons and brain systems.
o Brain emulation would help us to understand the brain, both in the lead‐up
to successful emulation and afterwards by providing an ideal test bed forneuroscientific experimentation and study.
o Neuromorphic engineering based on partial results would be useful in a
number of applications such as pattern recognition, AI and brain‐computerinterfaces.
o As a long‐term research goal it might be a strong vision to stimulate
computational neuroscience.
o As a case of future studies it represents a case where a radical future
possibility can be examined in the light of current knowledge.
Economics
o The economic impact of copyable brains could be immense, and could haveprofound societal consequences (Hanson, 1994, 2008b). Even low probability
events of such magnitude merit investigation.
Individuallyo If emulation of particular brains is possible and affordable, and if concerns
about individual identity can be met, such emulation would enable back‐up
copies and “digital immortality”.
Philosophyo Brain emulation would itself be a test of many ideas in the philosophy ofmind and philosophy of identity, or provide a novel context for thinking
about such ideas.
o It may represent a radical new form of human enhancement.

WBE represents a formidable engineering and research problem, yet one which appears tohave a well‐defined goal and could, it would seem, be achieved by extrapolations of current
technology. This is unlike many other suggested radically transformative technologies likeartificial intelligence where we do not have any clear metric of how far we are from success.
In order to develop ideas about the feasibility of WBE, ground technology foresight andstimulate interdisciplinary exchange, the Future of Humanity Institute hosted a workshop on
May 26 and 27, 2007, in Oxford. Invited experts from areas such as computational
neuroscience, brain‐scanning technology, computing, nanotechnology, and neurobiology
presented their findings and discussed the possibilities, problems and milestones that would
have to be reached before WBE becomes feasible.
The workshop avoided dealing with socioeconomic ramifications and with philosophicalissues such as theory of mind, identity or ethics. While important, such discussions would
undoubtedly benefit from a more comprehensive understanding of the brain—and it was thisunderstanding that we wished to focus on furthering during the workshop. Such issues will
likely be dealt with at future workshops.

5This document combines an earlier whitepaper that was circulated among workshop
participants, and additions suggested by those participants before, during and after theworkshop. It aims at providing a preliminary roadmap for WBE, sketching out key
technologies that would need to be developed or refined, and identifying key problems or
uncertainties.
Brain emulation is currently only a theoretical technology. This makes it vulnerable tospeculation, “handwaving” and untestable claims. As proposed by Nick Szabo, “falsifiable
design” is a way of curbing the problems with theoretical technology:…the designers of a theoretical technology in any but the most predictable of areas
should identify its assumptions and claims that have not already been tested in alaboratory. They should design not only the technology but also a map of theuncertainties and edge cases in the design and a series of such experiments and tests
that would progressively reduce these uncertainties. A proposal that lacks thisadmission of uncertainties coupled with designs of experiments that will reduce such
uncertainties should not be deemed credible for the purposes of any important
decision. We might call this requirement a requirement for a falsifiable design. (Szabo,
2007)
In the case of brain emulation this would mean not only sketching how a brain emulator
would work if it could be built and a roadmap of technologies needed to implement it, butalso a list of the main uncertainties in how it would function and proposed experiments to
reduce these uncertainties.
It is important to emphasize the long‐term and speculative nature of many aspects of thisroadmap, which in any case is to be regarded only as a first draft—to be updated, refined,
and corrected as better information becomes available. Given the difficulties anduncertainties inherent in this type of work, one may ask whether our study is not premature.
Our view is that when the stakes are potentially extremely high, it is important to apply thebest available methods to try to understand the issue. Even if these methods are relatively
weak, it is the best we can do. The alternative would be to turn a blind eye to what could turnout to be a pivotal development. Without first studying the question, how is one to form any
well‐grounded view one way or the other as to the feasibility and proximity of a prospect like
WBE?

Thanks to
We would like to warmly thank the many people who have commented on the paper andhelped extend and improve it:
Workshop participants: John Fiala, Robin Hanson, Kenneth Jeffrey Hayworth, Todd
Huffman, Eugene Leitl, Bruce McCormick, Ralph Merkle, Toby Ord, Peter Passaro, Nick
Shackel, Randall A. Koene, Robert A. Freitas Jr and Rebecca Roache.
Other useful comments: Stuart Armstrong.

6

The concept of brain emulation
Whole brain emulation, often informally called “uploading” or “downloading”, has been thesubject of much science fiction and also some preliminary studies (see Appendix D for history
and previous work). The basic idea is to take a particular brain, scan its structure in detail,and construct a software model of it that is so faithful to the original that, when run on
appropriate hardware, it will behave in essentially the same way as the original brain.

Emulation and simulation
The term emulation originates in computer science, where it denotes mimicking the functionof a program or computer hardware by having its low‐level functions simulated by anotherprogram. While a simulation mimics the outward results, an emulation mimics the internalcausal dynamics (at some suitable level of description). The emulation is regarded as
successful if the emulated system produces the same outward behaviour and results as theoriginal (possibly with a speed difference). This is somewhat softer than a strict mathematicaldefinition1.
According to the Church‐Turing thesis, a Turing machine can emulate any other Turingmachine. The physical Church‐Turing thesis claims that every physically computablefunction can be computed by a Turing machine. This is the basis for brain emulation: if brain
activity is regarded as a function that is physically computed by brains, then it should bepossible to compute it on a Turing machine. Even if true, however, it does not demonstratethat it is a computationally feasible process.
In the following, emulation will refer to a 1‐to‐1 model where all relevant properties of asystem exist, while a simulation will denote a model where only some properties exist.
Emulations may behave differently from each other or the original due to noise or intrinsic
chaos, but behave within the range of what one would expect from the original if it had
experienced the same noise or chaos.
By analogy with a software emulator, we can say that a brain emulator is software (andpossibly dedicated non‐brain hardware) that models the states and functional dynamics of abrain at a relatively fine‐grained level of detail.
In particular, a mind emulation is a brain emulator that is detailed and correct enough to
produce the phenomenological effects of a mind.

1 A strict definition of simulation might be that a system S consists of a state x(t) evolving by a particular dynamics f,
influenced by inputs and producing outputs: x(t+1) = f(I,x(t)), O(t)=g(x(t)). Another system T simulates S if it produces
the same output (within a tolerance) for the same input time series starting with a given state (within a tolerance):
X(t+1)=F(I, X(t)), O(t)=G(X(t)) where |x(t)‐X(t)|<ε1 and X(0)=x(0)+ ε2. The simulation is an emulation if F=f (up to abijective transformation of X(t)), that is, the internal dynamics is identical and similar outputs are not due to the formof G(X(t)).

Chaotic systems are not simulable by this definition, since after enough time they will diverge if the initial conditions
differ. Since even a three neuron system can become chaotic (Li, Yu et al., 2001) it is very plausible that the brain
contains chaotic dynamics and it is not strictly simulable. However, there exists a significant amount of noise in thebrain that does not prevent meaningful brain states from evolving despite the indeterminacy of their dynamics. A“softer” form of emulation may be possible to define that has a model or parameter error smaller than the noise level
and is hence practically indistinguishable from a possible evolution of the original system.

7

A person emulation is a mind emulation that emulates a particular mind.
What the “relevant properties” are is a crucial issue. In terms of software emulation this isoften the bits stored in memory and how they are processed. A computer emulator may
emulate the processor, memory, I/O and so on of the original computer, but does not simulate
the actual electronic workings of the components, only their qualitative function on the stored
information (and its interaction with the outside world). While lower‐level emulation ofcomputers may be possible it would be inefficient and not contribute much to the functions
that interest us.
Depending on the desired success criterion emulation may require different levels of detail. It
might also use different levels of detail in different parts of the system. In the computer
example, emulating the result of a mathematical calculation may not require simulating theexecution of all operating system calls for math functions (since these can be done more
efficiently by the emulating computer’s processor) while emulating the behaviour of ananalogue video effect may require a detailed electronics simulation.

Little need for whole-system understanding
An important hypothesis for WBE is that in order to emulate the brain we do not need tounderstand the whole system, but rather we just need a database containing all necessary
low‐level information about the brain and knowledge of the local update rules that change
brain states from moment to moment. A functional understanding (why is a particular pieceof cortex organized in a certain way) is logically separate from detail knowledge (how is itorganised, and how does this structure respond to signals). Functional understanding may be
a possible result from detail knowlede and it may help gather only the relevant information
for WBE, but it is entirely possible that we could acquire full knowledge of the component
parts and interactions of the brain without gaining an insight into how these produce (say)consciousness or intelligence.
Even a database merely containing the complete “parts list” of the brain, including themorphology of its neurons, the locations, sizes and types of synaptic connections, would be
immensely useful for research. It would enable data‐driven research in the same way as
genomics has done in the field of cell biology (Fiala, 2002).
Computational neuroscience attempts tounderstand the brain by making
mathematical or software models of neuralsystems. Currently, the models are usually
far simpler than the studied systems, withthe exception of some small neuralnetworks such as the lobster stomatogastricganglion (Nusbaum and Beenhakker, 2002)
and the locomotor network of the lamprey
spinal cord (Kozlov, Lansner et al., 2007).
Often models involve a combination of
simplified parts (simulated neurons andsynaptic learning rules) and network
structures (subsampling of biologicalneurons, simple topologies). Such
networks can themselves constitute

Figure 1: Understanding of function vs.
understanding of details.

8learning or pattern recognizing systems on their own, artificial neural networks (ANNs).
ANN models can be used to qualitatively model, explain and analyze the functions of brain
systems (Rumelhart, McClelland et al., 1986). Connectionist models build more complex
models of cognition or brain function on these simpler parts. The end point of this pursuit
would be models that encompass a full understanding of the function of all brain systems.
Such qualitative models might not exhibit intelligence or the complexity of human behaviour
but would enable a formalized understanding of how they come about from simple parts.
Another approach in computational neuroscience involves creating more biologically realistic
models, where information about the biological details of neurons such as theirelectrochemistry, biochemistry, detailed morphology and connectivity are included. At its
simplest we find compartment models of individual neurons and synapses, while more
complex models include multiple realistic neurons connected into networks, possibly taking
interactions such as chemical volume transmission into account. This approach can be seen asa quantitative understanding of the brain, aiming for a complete list of the biological parts(chemical species, neuron morphologies, receptor types and distribution etc.) and modellingas accurately as possible the way in which these parts interact. Given this information
increasingly large and complex simulations of neural systems can be created. WBE represents
the logical conclusion of this kind of quantitative model: a 1‐to‐1 model of brain function.
Note that the amount of functional understanding needed to achieve a 1‐to‐1 model is small.
Its behaviour is emergent from the low‐level properties, and may or may not be understoodby the experimenters. For example, if coherent oscillations are important for conceptual
binding and these emerge from the low‐level properties of neurons and their networks, acorrect and complete simulation of these properties will produce the coherence.
In practice computational neuroscience works in between quantitative and qualitativemodels. Qualitative models are used to abstract complex, uncertain and potentially irrelevant
biological data, and often provide significant improvements in simulation processing
demands (in turn enabling larger simulations, which may enable exploration of domains ofmore interest). Quantitative models are more constrained by known biology, chemistry andphysics but often suffer from an abundance of free parameters that have to be set (Herz,
Gollisch et al., 2006). Hybrid models may include parts using different levels of abstraction,
or exist as a family of models representing the same system at different levels of abstraction.
The interplay between biological realism (attempting to be faithful to biology), completeness(using all available empirical data about the system), tractability (the possibility ofquantitative or qualitative simulation) and understanding (producing a compressed
representation of the salient aspects of the system in the mind of the experimenter) will often
determine what kind of model is used. The appropriate level of abstraction and method of
implementation depends on the particular goal of the model. In the case of WBE, the successcriteria discussed below place little emphasis on understanding, but much emphasis on
qualitatively correct dynamics, requiring much biological realism (up to a point, set by scale
separation) and the need for data‐driven models. Whether such models for whole brain
systems are tractable from a modelling and simulation standpoint is the crucial issue.
Brain emulation cannot be achieved without some functional understanding. It needs models
and theories for recognizing what data is relevant, and would provide data for developing
and testing these further. While in theory brain emulation might hug the lower line in Figure
1, in practice it will likely occur somewhere along the right edge – still far below a fullunderstanding of the top level phenomena, but including a broad understanding of many9

kinds of low level phenomena. We also need some understanding of higher level phenomena
to test our simulations and know what kind of data we need to pursue. Fostering the rightresearch cycle for developing the right understanding, collecting data, improving
instrumentation, and experimenting with limited emulations – in addition to providing
useful services to related fields and beneficial spin‐offs – would be indispensable for thedevelopment of WBE.

Levels of emulation and success criteria
6aSocial role-fit
emulation

6b
Mind emulation

5
Individual brain
emulation

4
Species
generic brain
emulation

3
Functional
brain emulation

2
Brain database

6cPersonal
identityemulation

For the brain, several levels of
success criteria for emulation
can be used. They form ahierarchy extending fromlow‐level targets to completeemulation. See Table 1 on
page 11.
Not shown in this hierarchy
are emulations of subsystems
or small volumes of the brain,“partial brain emulations”.
Properly speaking, acomplete scan, parts list andbrain database (1a, 1b and 2)
do not constitute successful
brain emulation, but suchachievements (and partial
brain emulations) would inany case be importantmilestones and useful inthemselves.

Similarly, the high‐level
achievements related to social
1a1bParts List
Complete scanroles, mental states, andpersonal identify (6a, 6b and6c) are both poorly‐
understoodand hard to
Figure 2: Success levels for WBE.
operationalize, but given thephilosophical interest in WBE we have included them here for completeness. It is not obvious
how these criteria relate to one another, or to what extent they might be entailed by thecriteria for 4 and 5.
Achieving the third success criterion beyond a certain resolution would, assuming somesupervenience thesis, imply success of some or all of the other criteria. A full quantum‐
mechanical N‐body or field simulation encompassing every particle within a brain would
plausibly suffice even if “quantum mind” theories are correct. At the very least a 1‐to‐1
material copy of the brain (a somewhat inflexible and very particular kind of emulating
computer) appears to achieve all criteria, possibly excepting those for 6c. However, this is10likely an excessively detailed level since the particular phenomena we are interested in (brain
function, psychology, mind) appear to be linked to more macroscopic phenomena thandetailed atomic activity.
Given the complexities and conceptual issues of consciousness we will not examine criteria
6abc, but mainly examine achieving criteria 1‐5.
Table 1: Success Criteria
1a

Level
“Parts list”

Success criterion
An inventory of all objects on a particularsize scale, their properties and interactions.

1b“Complete scan”

2“Brain database”

A complete 3D scan of a brain at highresolution.
Combining the scan and parts list into a
database mapping the low level objects of abrain.

3“Functional brain
emulation”

4“Species generic brain
emulation”

5“Individual brain
emulation”

The emulation produces emergent activity
characteristic of that of one particular (fully
functioning) brain. It is more similar to theactivity of the original brain than any otherbrain.

6a“Social role‐fit
emulation”/“Person
emulation”

The emulation is able to fill and be accepted
into some particular social role, for example
to perform all the tasks required for somenormally human job. (Socio‐economic
criteria involved)

6b“Mind emulation”

The emulation produces subjective mental
states (qualia, phenomenal experience) of thesame kind that would have been producedby the particular brain being emulated.
(Philosophical criteria involved)

6c“Personal identity
emulation”

The emulation is correctly described as acontinuation of the original mind; either as
numerically the same person, or as asurviving continuer thereof. (Philosophicalcriteria involved)

The emulation simulates the objects in abrain database with enough accuracy to
produce (at least) a substantial range of
species‐typical basic emergent activity of thesame kind as a brain (e.g. a slow wave sleep
state or an awake state).
The emulation produces the full range of(human) species‐typical emergent behavior
and learning capacity.

Relevant properties
Low level neural structure,chemistry, dynamics accurate toresolution level.
Resolution, information enabling
structure to function mapping.
1‐to‐1 mapping of scan tosimulation/emulation objects.

Generically correct causal micro‐
dynamics.

Long term dynamics andadaptation. Appropriate
behaviour responses. Full‐range
learning capacity.
Correct internal and behaviour
responses. Retains mostmemories and skills of theparticular brain that wasemulated. (In an emulation of ananimal brain, it should bepossible to recognize theparticular (familiar) animal.)
Properties depend on which(range of) social roles theemulation would be able to fit. Ina limiting case, the emulation
would be able to pass a
personalized Turing test:
outsiders familiar with theemulated person would beunable to detect whether
responses came from original
person or emulation.
The emulation is truly conscious
in the same way as a normal
human being.

The emulation is an object ofprudentially rational self‐concern
for the brain to be emulated.

11

Scale separation
At first it may appear unlikely that a complex system with many degrees of freedom like thebrain could be modelled with the right causal dynamics, but without taking into account thesmallest parts. Microstimulation of individual neurons can influence sensory decisions(Houweling and Brecht, 2008), showing that very small disturbances can – under the rightcircumstances – scale up to behavioural divergences.
However, state variables of complex systems can be quantitatively predicted when there is‘scale separation’: when different aspects of the system exist on sufficiently (orders ofmagnitude) different scales (of size, energy, time etc), they can become uncoupled(Hillerbrand, 2008). A typical example is how the microscopic dynamics of a laser (atoms
interacting with an oscillating electromagnetic field) gives rise to a macroscopic dynamics(the growth and decay of different laser modes) in such a way that an accurate simulation of
the system using only elements on the macroscale is possible. Another example is the scale
separation between electric currents and logic operations in a computer, which enables bit‐based emulation. When there is no scale separation (such as in fluid turbulence) macroscalepredictions become impossible without simulating the entire microscale.
An important issue to be determined is whether such a cut‐off exists in the case of the humanbrain and, if it does exist, at what level. While this paper phrases it in terms ofsimulation/emulation, it is encountered in a range of fields (AI, cognitive neuroscience,philosophy of mind) in other forms: what level of organisation is necessary for intelligent,personal, or conscious behaviour?
A key assumption of WBE is that, at some intermediary level of simulation resolution
between the atomic and the macroscopic, there exists at least one cut‐off such that meeting
criteria 1a and 1b at this level of resolution also enables the higher criteria to be met.
At such a spatial, temporal, or organisational scale, the dynamics on the larger/slower scale is
not functionally sensitive to the dynamics of the smaller/faster scale. Such scale separation
might occur at the synaptic scale, where the detailed chemical dynamics underlying synaptic
function could be replaced by a simplified qualitative model of its effects on signals andsynaptic strengths. Another possible scale separation level might occur between individual
molecules and molecular concentration scales: molecular dynamics could be replaced withmass‐action interactions of concentrations. A perhaps less likely
separation could also occur on higher levels if what matters is theactivity of cortical minicolumns rather than individual neurons. A
final likely but computationally demanding scale or separation
would be the atomic scale, treating the brain emulation as a N‐bodysystem of atoms.
Conversely, if it could be demonstrated that there is no such scale, itwould demonstrate the infeasibility of whole brain emulation. Due
to causally important influence from smaller scales in this case, asimulation at a particular scale cannot become an emulation. The
causal dynamics of the simulation is not internally constrained, so it
is not a 1‐to‐1 model of the relevant dynamics. Biologicallyinteresting simulations might still be possible, but they would be
local to particular scales and phenomena, and they would not fully
reproduce the internal causal structure of the whole brain.

Figure 3: Size scales in
the nervous system.

12

Simulation scales
The widely reproduced diagram from (Churchland and Sejnowski, 1992) in Figure 3 depicts
the various levels of organisation in the nervous system ordered by size scale, running fromthe molecular level to the entire system. Simulations (and possibly emulations) can occur on
all levels:
•
•
•
•
•
•
•

Molecular simulation (individual molecules)
Chemistry simulation (concentrations, law of mass action)
Genetic expression
Compartment models (subcellular volumes)
Whole cell models (individual neurons)
Local network models (replaces neurons with network modules such as
minicolumns)
System models

Another hierarchy was introduced by John Fiala during the workshop, and will be used withsome adaptations in this document.
Table 2: Levels of emulation
Level
1
2
3

Computational
moduleBrain region
connectivity
Analog network
population model4

Spiking neuralnetwork

5

Electrophysiology

6

Metabolome

7
8

Proteome
States of protein
complexes
Distribution ofcomplexes
Stochastic behaviourof single molecules
Quantum

910
11“Classic AI”, high level representations of information and information
processing.
Each area represents a functional module, connected to others according
to a (species universal) “connectome” (Sporns, Tononi et al., 2005).
Neurons populations and their connectivity. Activity and states ofneurons or groups of neurons are represented as their time‐averages. Thisis similar to connectionist models using ANNs, rate‐model neural
simulations and cascade models.
As above, plus firing properties, firing state and dynamical synaptic states.
Integrate and fire models, reduced single compartment models (but also
some minicolumn models, e.g. (Johansson and Lansner, 2007)).
As above, plus membrane states (ion channel types, properties, state), ionconcentrations, currents, voltages and modulation states. Compartment
model simulations.
As above, plus concentrations of metabolites and neurotransmitters incompartments.
As above, plus concentrations of proteins and gene expression levels.
As above, plus quaternary protein structure.
As above, plus “locome” information and internal cellular geometry.
As above plus molecule positions, or a molecular mechanics model of theentire brain.
Quantum interactions in and between molecules.

The amount of understanding needed to accurately simulate the relevant objects tends toincrease radically for higher (here, low‐numbered) levels: while basic mechanics is wellunderstood, membrane biophysics is complex, and the computational functions of brain areas
are likely exceedingly multifaceted. Conversely, the amount of computing power needed
increases rapidly as we descend towards lower levels of simulation, and may become
fundamentally infeasible on level 112. The amount and type data needed to fully specify a
2 But see also the final chapter of (Hameroff, 1987). The main stumbling block of level 11 simulation may not becomputing hardware or understanding but fundamental quantum limitations on scanning.

13

model also changes character between the different levels. Low‐level simulations requiremassive quantities of simple information (molecular positions and types) whereas higher
levels require a smaller amount of very complex information (content of mental processes).
Each level has its own characteristic size and time scale, restricting the required imaging
resolution and simulation timestep. For example, synaptic spine necks and the thinnest axons
can be on the order of 50 nm or smaller, requiring imaging on the order of the 5 nanometerscale to resolve them.
An informal poll among workshop attendees produced a range of estimates of the required
resolution for WBE is. The consensus appeared to be level 4‐6. Two participants were more
optimistic about high level models, while two suggested that elements on level 8‐9 may benecessary at least initially (but that the bulk of mature emulation, once the basics were
understood, could occur on level 4‐5). To achieve emulation on this level, the consensus wasthat 5×5×50 nm scanning resolution would be needed. This roadmap will hence focus on level
4‐6 models, while being open for that deeper levels may turn out to be needed.
As noted by Fiala, WBE likely requires at least level 4 to capture the specificity of individual
brains, but probably requires complexity at level 6 or lower to fully capture thecomputational contributions of ion channels, second messengers, protein level adaptation,and stochastic synaptic transmission. Other participants thought that at least level 5 would beneeded for individual brain properties.

Forecasting
Analysing the requirements for emulation (in terms of scanning method, number of entities
to simulate, resulting storage requirements and computational demands) at each of the levels
provides a way of bounding progress towards WBE. Given these estimates and scenarios of
future progress in scanning and computing it is possible to calculate the earliest point in time
where there is enough resources to produce a WBE on a given level at a certain price. As
better information becomes available such estimates can be refined.
Although any time estimates will be subject to strong uncertainties, they can be helpful inestimating how far away WBE is from policy‐relevant timescales, as well as likely timeframes
for early small‐scale emulations. They also allow comparisons to other technology forecasts,
enabling estimation of the chances for synergies (e.g. the development of molecular
nanotechnology, which would accelerate WBE progress), reliability (e.g. the further into the
future, the more unlikely Moore’s law is to hold), and the risk of other technologies
overtaking WBE (e.g. artificial intelligence).
Early WBE may require lower‐level simulation than later forms, as there might not yet beenough experience (and test systems) to determine which simulation elements are strictly
necessary for success. The main concern in this document is estimating when and how WBE
will first be achieved rather than its eventual mature or “best” form.

14

WBE assumptions
Philosophical assumptions
Physicalism (everything supervenes on the physical) is a convenient but not necessary
assumption, since some non‐physicalist theories of mental properties could allow them toappear in the case of WBE. Success criterion 6b emulation assumes multiple realizability(that the same mental property, state, or event can be implemented by different physicalproperties, states, and events). Sufficient apparent success with WBE would provide
persuasive evidence for multiple realizability. Generally, emulation up to and including level
6a does not appear to depend on any strong metaphysical assumptions.

Computational assumptions
Computability: brain activity is Turing‐computable, or if it is uncomputable, theuncomputable aspects have no functionally relevant effects on actual behaviour.
Non‐organicism: total understanding of the brain is not required, just component parts andtheir functional interactions.
Scale separation: at some intermediary level of simulation resolution between the atomic andthe macroscopic there exists one (or more) cut‐offs such that meeting criterion 2 at this level issufficient for meeting one or more of the higher criteria.
Component tractability: the actual brain components at the lowest emulated level can beunderstood well enough to enable accurate simulation.
Simulation tractability: simulation of the lowest emulated level is computationally tractablewith a practically realizable computer.

Neuroscience assumptions
Brain‐centeredness: in order to produce accurate behaviour only the brain and some parts ofthe body need to be simulated, not the entire body.

WBE appears to be a way of testing many of these assumptions experimentally. In acquiring
accurate data about the structure and function of the brain and representing it as emulations
it should be possible to find major discrepancies if, for example, Computability is not true.

15

Roadmap
Requirements
WBE requires three main capabilities: the ability to physically scan brains in order to acquire
the necessary information, the ability to interpret the scanned data to build a software model,
and the ability to simulate this very large model. These in turn require a number ofsubcapabilities (Table 3: Capabilities needed for WBE).
Plausible scanning methods require ways of preparing the brains, in particular separationfrom other tissue, fixation and possibly dyeing. There is also a need for methods of physicallyhandling and storing pieces of tissue: since most scanning methods cannot image large
volumes the brains will have to be sectioned into manageable pieces. This must allow
corresponding cells and dendrites to be identified on both sides. While fixation andsectioning methods are commonly used in neuroscience, the demands for whole brain
emulation are stricter: much larger volumes must be handled with far less tolerance fordamage.
Imaging methods are discussed in more detail in the chapter on scanning. The three key
issues are achieving the necessary resolution to image the smallest systems needed for an
emulation, the ability to image (not necessarily simultaneously) the entire brain, and theability to acquire the functionally relevant information.
Translating the data from the imaging process into software requires sophisticated image
processing, the ability to interpret the imagery into simulation‐relevant parameters, andhaving a computational neuroscience model of sufficient precision. The image processing will
have to deal with the unavoidable artefacts from scanning such as distortions and noise, as
well as occasional lost data. It will likely include methods of converting direct scan data into
more compressed forms, such as traced structures, in order to avoid excessive storage needs.
The scan interpretation process makes use of this data to estimate the connectivity, and to
identify synaptic connections, cell types and simulation parameters. It then places thisinformation in an “inventory database” for the emulation. These steps are discussed in theimage processing chapter.
The software model requires both a mathematical model of neural activity and ways ofefficiently implementing such models on computers (discussed in the chapter on neural
simulation). Computational neuroscience aims at modelling the behaviour of neural entities
such as networks, neurons, synapses and learning processes. For WBE, it needs to havesufficiently good models of all relevant kinds of subsystems, along with the relevant
parameters set from scan data in order to construct a computational model of the actual brainthat was scanned.
To emulate a brain, we need enough computing power to run the basic emulation software, asufficiently realistic body simulation, and possibly a simulated environment. The key
demands are for memory storage to hold the information and processor power to run it at asuitable speed. The massive parallelism of the problem will put some significant demands on
the internal bandwidth of the computing system.
In addition, WBE likely requires the development of three supporting technology areas, withwhich it has a symbiotic relationship. First, validation methods to check that other steps

16

produce accurate data and models. This includes validation of scanning, validation of scaninterpretation, validation of neuroscience models, validations of implementation, and ways oftesting the success of WBE. While ordinary neuroscience research certainly aims at validation,
it does not systematize it. For a complex multi‐step research effort like WBE, integrated
validation is likely necessary to ensure that bad data or methods do not confuse later steps inthe process. Second, WBE requires significant low‐level understanding of neuroscience inorder to construct the necessary computational models and scan interpretation methods. Thisis essentially a continuation and strengthening of systems biology and computational
neuroscience aiming at a very complete description of the brain on some size or functional
scale. Third, WBE is large‐scale neuroscience, requiring methods of automatingneuroscientific information gathering and experimentation. This will reduce costs andincrease throughput, and is necessary in order to handle the huge volumes of data needed.
Large‐scale/industrial neuroscience is clearly relevant for other neuroscience projects too.

17

Preparation

Scanning

Physical handling

Resolution

Imaging

VolumeFunctional
information

Geometric
adjustment

Noise removal

Data interpolation

Tracing

Image processing

Whole
brain
emulation

Mathematicalmodel
Translation

Software model ofneural system
Efficient
implementation

Parameter
estimation
Synapseidentification
Scaninterpretation

Connectivity
identification
Cell typeidentification
Databasing

Environment
simulation
CPU
Simulation

Body Simulation
Bandwidth
Storage

Figure 4: Technological capabilities needed for WBE.

18

Table 3: Capabilities needed for WBE
Preprocessing/fixation

Physical handling
Scanning

VolumeImaging

Resolution
Functional information
Geometric adjustment

Image processing

Data interpolation
Noise removal
Tracing
Cell type identification

Translation

Synapse identification
Scan interpretation

Parameter estimation
Databasing

Software model of neuralsystemStorage
Bandwidth
Simulation

CPU
Body simulation
Environment simulation

Mathematical model
Efficient implementation

Preparing brains appropriately,
retaining relevant
microstructure and state
Methods of manipulating fixed
brains and tissue pieces before,
during, and after scanning
Capability to scan entire brain
volumes in reasonable timeand expense.
Scanning at enough resolution
to enable reconstruction
Scanning is able to detect thefunctionally relevant propertiesof tissue
Handling distortions due toscanning imperfection
Handling missing data
Improving scan quality
Detecting structure andprocessing it into a consistent
3D model of the tissue
Identifying cell types
Identifying synapses and theirconnectivity
Estimating functionally
relevant parameters of cells,
synapses, and other entities
Storing the resulting inventory
in an efficient way
Model of entities and their
behaviour
Implementation of model
Storage of original model andcurrent state
Efficient inter‐processor
communication
Processor power to run
simulation
Simulation of body enabling
interaction with virtualenvironment or through robot
Virtual environment for virtualbody

Linkages
Most of the capabilities needed for WBE are independent of each other, or form synergistic
clusters. Clusters of technologies develop together, supporting and motivating each otherwith their output. A typical example is better mathematical models stimulating a need forbetter implementations and computing capacity, while improvements in the latter two
stimulate interest in modelling. Another key cluster is 3D microscopy and image processing,where improvements in one makes the other more useful.
There are few clear cases where a capability needs a completed earlier capability in order tobegin development. Current fixation and handling methods are likely unable to meet the19demands of WBE‐level 3D microscopy, but are good enough to enable early development for
certain small systems. Scan interpretation needs enough scan data to develop methods, but
given current research the bottleneck appears to be more on the image processing andinterpretation side than data availability. Achieving large volume scanning requires
parallelization and scaling up previous scanning methods, for example by using robotic workand parallel microscopy. This requires researchers thinking in terms of, and having
experience with, an “industrial” approach to data collection.
This interlinked nature of the field avoids any obvious technology thresholds andbottlenecks. There is no one technology that must be developed before other technologies canadvance. Development can occur on a broad front simultaneously, and rapid progress in afield can promote feedback progress in related fields. Unfortunately, it also means that slow
progress in one area may hold back other areas, not just due to lack of results but by reduced
demand for their findings, reduced funding, and focus on research that does not lead in the
direction of WBE.

Roadmap
Based on these considerations we can sketch out a roadmap with milestones, required
technologies, key uncertainties and external technology interactions.
Approach to WBE has two phases. The first phase consists of developing the basic capabilities
and settling key research questions that determine the feasibility, required level of detail andoptimal techniques. This phase mainly involves partial scans, simulations and integration of
the research modalities.
The second phase begins once the core methods have been developed and an automatedscan‐interpretation‐simulate pipeline has been achieved. At this point the first emulations
become possible. If the developed methods prove to be scalable they can then be applied toincreasingly complex brains. Here the main issue is scaling up techniques that have alreadybeen proven on the small scale.

20

Humanemulation

Large mammal
emulation

Validation
methods

Ground truth
modelsAppropriate
level
Small mammal
emulation

Deducing
function
Invertebrateemulation
Scanning
development

Completeinventory
Partialemulations

Interpretation
development

Automatedpipeline

Low-levelneuroscience

Eutelicorganismemulation

Organism
simulation

Full cell
simulation

Body
simulation

Simulation
hardwareFigure 5: WBE roadmap.
The key milestones are:Ground truth models: a set of cases where the biological “ground truth” is known and can becompared to scans, interpretations and simulations in order to determine their accuracy.
Determining appropriate level of simulation: this includes determining whether there exists
any suitable scale separation in brains (if not, the WBE effort may be severely limited), and if
so, on what level. This would then be the relevant scale for scanning and simulation.
Full cell simulation: a complete simulation of a cell or similarly complex biological system.
While strictly not necessary for WBE it would be a test case for large‐scale simulations.

21

Body simulation: an adequate simulation for the model animal’s body (and environment).
Ideally demonstrated by “fooling” a real animal connected to it.
Simulation hardware: special‐purpose simulation/emulation computer hardware may befound to be necessary or effective.
Organism simulation: a simulation of an entire organism in terms of neural control, bodystate and environmental interaction. This would not be a true emulation since it is not basedon any individual but rather known physiological data for the species. This would enable
more realistic and individual models as scans, models and computer power improves.
Demonstration of function deduction: demonstrating that all relevant functional properties
on a level can be deduced from scan data.
Complete inventory: a complete database of entities at some level of resolution for a neuralsystem, e.g. not just the connectivity of the C. elegans nervous system but also theelectrophysiology of the cells and synapses. This would enable full emulation if all the update
rules are known. It demonstrates that the scanning and translation methods have matured.
Automated pipeline: a system able to produce a simulation based on an input tissue sample,
going through the scan, interpretation and simulation steps without major humanintervention. The resulting simulation would be based on the particular tissue rather thanbeing a generic model.
Partial emulation: A complete emulation of neural system such as the retina, invertebrate
ganglia or a V1 circuit based on scanned and interpreted data from a brain rather than species
data. This would demonstrate the feasibility of data‐driven brain emulation.
Eutelic organism emulation: a complete emulation of a simple organism, such as C. elegans oranother eutelic (fixed nervous system) organism using data from pipeline scanning. It may
turn out that it is unnecessary to start with a eutelic organism and the first organism
emulation would be a more complex invertebrate.
Invertebrate WBE: Emulation of an invertebrate such as a snail or an insect, with learning.
This would test whether the WBE approach can produce appropriate behaviours. If thescanned individual was trained before scanning, retention of trained responses can bechecked.
Small mammal WBE: Demonstration of WBE in mice or rats, proving that the approach canhandle mammalian neuroanatomy.
Large mammal WBE: Demonstration in higher mammals, giving further information about
how well individuality, memory and skills are preserved as well as investigation of safetyconcerns.
Human WBE: Demonstration of an interactive human emulation.

22

Technology drivers
Preparation

Scanning

Physical handling

Resolution

Imaging

VolumeFunctional
information

Geometric
adjustment

Noise removal

Data interpolation

Tracing

Image processing

Whole
brain
emulation

Mathematicalmodel
Translation

Software model ofneural system
Efficient
implementation

Parameter
estimation
Synapseidentification
Scaninterpretation

Connectivity
identification
Cell typeidentification
Databasing

Environment
simulation
CPU
Simulation

Body Simulation
Bandwidth
Storage

Moore’s law
drivenCommercial
drivers

Research
drivers

WBE specific?

Figure 6: Technology drivers for WBE‐necessary technologies.
Different required technologies have different support and drivers for development.

23

Computers are developed independently of any emulation goal, driven by mass market
forces and the need for special high performance hardware. Moore’s law and related
exponential trends appear likely to continue some distance into the future, and the feedback
loops powering them are unlikely to rapidly disappear (see further discussion in Appendix B:
Computer Performance Development). There is independent (and often sizeable) investment
into computer games, virtual reality, physics simulation and medical simulations. Like
computers, these fields produce their own revenue streams and do not require WBE‐specificor scientific encouragement.
A large number of the other technologies, such as microscopy, image processing, andcomputational neuroscience are driven by research and niche applications. This means lessfunding, more variability of the funding, and dependence on smaller groups developing
them. Scanning technologies are tied to how much money there is in research (including
brain emulation research) unless medical or other applications can be found. Validation
techniques are not widely used in neuroscience yet, but could (and should) become standard
as systems biology becomes more common and widely applied.
Finally there are a few areas relatively specific to WBE: large‐scale neuroscience, physicalhandling of large amounts of tissue blocks, achieving high scanning volumes, measuringfunctional information from the images, automated identification of cell types, synapses,
connectivity and parameters. These areas are the ones that need most support in order toenable WBE.
The latter group is also the hardest to forecast, since it has weak drivers and a small numberof researchers. The first group is easier to extrapolate by using current trends, with theassumption that they remain unbroken sufficiently far into the future.

Uncertainties and alternatives
The main uncertainties in the roadmap are:
Does scale separation enabling WBE occur? This is a basic science question, and if scale
separation does not occur at a sufficiently high scale‐level then WBE would be severely
limited or infeasible. It is possible that progress in understanding complex systems in general
will help clarify the situation. The question of which complex systems are simulable underwhich conditions is of general interest for many fields. However, in relation to WBE theanswer seems most likely to come from advances in computational neuroscience and fromtrial‐and‐error. By experimenting with various specialized neural emulations, at different
levels of resolution, and comparing the functionally‐relevant computational properties of theemulation with those of the emulated subsystem, we can test whether a given emulation is
successful. If so, we can infer that a sufficient scale separation for that subsystem exists at (orabove) the scale level (granularity) used in the emulation. We emphasize that a successful
emulation need not predict all details of the original behavior of the emulated system; it need only
replicate computationally relevant functionality at the desired level of emulation
What levels are possible/most appropriate for emulation? This will determine both therequirements for scanning and emulation software. In order to discover it, small‐scale
scanning and simulation projects need to be undertaken, developing skills and methods.
Later, full‐scale emulations of small systems will test whether the estimates hold. If an earlyanswer can be found, efforts can focus on this level; otherwise the WBE research front would
have to work on multiple levels.

24

How much of the functionally relevant information can be deduced from scanning in aparticular modality (e.g. electron microscopy)? At present, electron microscopy appears tobe the only scanning method that has the right resolution to reach synaptic connectivity, but
it is limited in what chemical state information it can reveal. If it is possible to deduce thefunction of a neuron, synapse or other structure through image interpretation methods, thenscanning would be far simpler than if this is not (in which case some form of hybrid method
or entirely new scanning modality would have to be developed). This issue appears to form apotentially well‐defined research question that could be pursued. Answering it would require
finding a suitable model system for which ground truth (the computational functionality oftarget system) was known, using the scanning modality to produce imagery and then testingout various forms of interpretation on the data.

Cellular dataandhypotheses

Wet
experiments

Qualitativemodelling

Quantitativemodelling

Cellprogramming

Large-scale
scanning

Simulation
Interpretation

Analysis
Large-scale
simulation

Figure 7: WBE computational biology research cycle (based on (Takahashi, Yugi et al.
2002)). WBE introduces large‐scale scanning and simulation into the cycle.
Developing the right research cycle. The computational biology research cycle today
involves wet experiments providing cellular data and hypotheses, which drive qualitativemodelling. This modelling in turn is used in quantitative modelling, which using simulations
generate data that can be analysed and employed to refine the models, compare with theexperiments, and suggest new experiments (Takahashi, Yugi et al., 2002). The WBE paradigm
incorporates this research cycle (especially in the software modelling part), but includes two
new factors. One is large‐scale scanning and processing of brain tissue, providing massive
amounts of data as input to the cycle, but also requiring the models to guide the developmentof scanning methods. The second is the large‐scale data driven simulations that do not aim at
producing just hypothesis testing and model refinement, but also at accurately mimicking thewet system. Both factors will increase and change the demands for data management,
hypothesis searching and simulation analysis/interpretation. They will also introduce
sociological and interdisciplinary factors, as different academic disciplines with very different
methodologies will have to learn how to communicate and cooperate. In order to be viable,
field research methods of testing, data‐sharing, validation, and standards for what constitutes
a result must be developed so that the extended cycle provides incentives for all participants
to cooperate and push the technology forward. This is closely linked to the likely move25

towards large‐scale neuroscience, where automated methods will play an increasingly
prominent role as they have done in genomics.

Figure 8: Caenorhabditis elegans, a popular model organism with a fully mapped 302
neuron nervous system.
Selection of suitable model systems. Selecting the right targets for scanning and modelling
will have to take into account existing knowledge, existing research communities, likelihoodof funding and academic impact as well as practical factors. While the C. elegans nervous
system has been completely mapped (White, Southgate et al., 1986), we still lack detailed
electrophysiology, likely because of the difficulty of investigating the small neurons. Animals
with larger neurons may prove less restrictive for functional and scanning investigation but
may lack sizeable research communities. Molluscs such as freshwater snails and insects suchas fruit flies may be promising targets. They have well characterised brains, existing researchcommunities and neural networks well within current computational capabilities.
Similarly, the selection of subsystems of the brain to study requires careful consideration.
Some neural systems are heavily studied (cortical columns, the visual system, thehippocampus) and better data about them would be warmly received by the researchcommunity, yet the lack of characterization of their inputs, outputs and actual function may
make development of emulation methods very hard. One system that may be very promising
is the retina, which has an accessible geometry, is well studied and somewhat wellunderstood, is not excessively complex, and better insights into which would be useful to awide research community. Building on retinal models, models of the lateral geniculate
nucleus and visual cortex may be particularly useful, since they would both have relatively
well‐defined inputs from the previous stages.
At what point will the potential be clear enough to bring major economic actors into WBE?
Given the potentially huge economic impact of human WBE (Hanson, 1994, 2008a, 2004,
2008b), if the field shows sufficient promise, major economic actors will become interested infunding and driving the research as an investment. It is unclear how far advanced the fieldwould need to be in order to garner this attention. Solid demonstrations of key technologies
are likely required, as well as a plausible path towards profitable WBE. The impact of funding
on progress will depend on the main remaining bottlenecks and their funding elasticity. If
scanning throughput or computer power is the limiting factor, extra funding can relatively
easily scale up facilities. By contrast, limitations in neuroscience understanding are lessresponsive to investment. If funding arrives late, when the fundamental problems havealready been solved, the amplified resources would be used to scale up pre‐existing small‐scale demonstration projects.

26Intellectual property constitutes another important consideration for commercial funding:what could early developers own, how secure and long‐term would their investment be?
Without solid prospects of having preferential ownership of what it develops, a firm isunlikely to pursue the project.

Alternative pathways
Special hardware for WBE. It is possible that WBE can be achieved more efficiently using
dedicated hardware rather than generic hardware. Such performance gains are possible if, forexample, there is a close mapping between the hardware and the brain, or if the functions of
the emulation software could be implemented efficiently physically. Developing suchdedicated hardware would be costly unless other applications existed (which is why interestin dedicated neural network chips peaked in the early 1990’s).
Dedicated neural network chips have reached up to 1.7 billion synaptic updates (and 337
million synaptic adjustments) per second for ANN models (Kondo, Koshiba et al., 1996),
which is approaching current supercomputing speeds for more complex models. Recently,
there has been some development of FPGAs (Field‐Programmable Gate Arrays) for runningcomplex neuron simulations, producing an order of magnitude faster simulation for a
motorneuron than a software implementation (four times real‐time, 8M compartments/s)(Weinstein and Lee, 2005). A FPGA implementation has the advantage of being
programmable, not requiring WBE‐special purpose hardware. Another advantage include
that as long as there is chip space, more complex models do not require more processing time
and that precision can be adjusted to suit the model and reduce space requirements.
However, scaling up to large and densely interconnected networks will require developing
new techniques (Weinstein and Lee, 2006). A better understanding of the neocorticalarchitecture may serve to produce hardware architectures that fit it well (Daisy project, 2008).
It has been suggested that using FPGAs could increase computational speeds in network
simulations by up to two orders of magnitude, and in turn enable testing grounds fordeveloping special purpose WBE chips (Markram, 2006).
It may also be possible to use embedded processor technology to manufacture large amountsof dedicated hardware relatively cheaply. A study of high resolution climate modelling in thepetaflop range found a 24‐ to 34‐fold reduction of cost and about two orders of magnitude
smaller power requirements using a custom variant of embedded processor chips (Wehner,
Oliker et al., 2008).
This roadmap is roughly centred on the assumption that scanning technology will be similar
to current microscopy, developed for large‐scale neuroscience, automated sectioning offixated tissue and local image‐to‐model conversion. For reasons discussed in the scanning
section, non‐destructive scanning of living brains appears to be hard compared to the “slice‐
and‐dice” approach where we have various small‐scale existence proofs. However, as pointed
out by Robert Freitas Jr., nanomedical techniques could possibly enable non‐destructive
scanning by use of invasive measurement devices. Even if such devices prove infeasible,
molecular nanotechnology could likely provide many new scanning methodologies as wellas radical improvement of neuroscientific research methods and the efficiency of manyroadmap technologies. Even far more modest developments such as single molecule analysis,
nanosensors, artificial antibodies and nanoparticles for imaging (which are expected to be inuse by 2015 (Nanoroadmap Project, 2006)) would have an important impact. Hence early orvery successful nanotechnology would offer faster and alternative routes to achieve the27roadmap. Analysing the likelihood, timeframe, and abilities of such nanomedicine is outside
the scope of this document.
One possible scanning alternative not examined much here is high resolution scanning offrozen brains using MRI. This might be a complement to destructive scanning, but could
possibly gain enough information to enable WBE. However, we currently have little
information on the limits and possibilities of the technique (see discussion in Appendix E:
Non‐destructive and gradual replacement).
As discussed in the overview, WBE does not assume any need for high‐level understandingof the brain or mind. In fact, should such understanding be reached it is likely that it could be
used to produce artificial intelligence (AI). Human‐level AI (or superintelligent AI) would
not necessarily preclude WBE, but some of the scientific and economic reasons would vanish,
possibly making the field less relevant. On the other hand, powerful AI could greatly
accelerate neuroscience advances and perhaps help develop WBE for other purposes.
Conversely, success in some parts of the WBE endeavour could help AI, for example if
cortical microcircuitry and learning rules could be simulated efficiently as a generallearning/behaviour system.
The impact and development of WBE will depend on which of the main capabilities(scanning, interpretation, simulation) develop last. If they develop relatively independently
it would be unlikely for all three to mature enough to enable human‐level emulations at thesame time. If computing power is the limiting factor, increasingly complex animal emulations
are likely to appear. Society has time to adapt to the prospect of human‐level WBE in the near
future. If scanning resolution, image interpretation, or neural simulation is the limiting factor,
a relatively sudden breakthrough is possible: there is enough computing power, scanningtechnology, and software to go rapidly from simple to complex organisms, using relatively
small computers and projects. This could lead to a surprise scenario wherein society has little
time to consider the possibility of human‐level WBE. If computing power is the limiting
factor, or if scanning is the bottleneck due to lack of throughput, then the pace ofdevelopment would likely be economically determined: if enough investment were made,WBE could be achieved rapidly. This would place WBE enablement under political or
economical control to a greater degree than in the alternative scenarios.

Related technologies and spin-offs
The technologies needed to achieve WBE include the ability to scan organic tissue on a low
level, interpret the findings into functional models, and run extremely large‐scale
simulations. WBE also requires sufficient knowledge of low‐level neural function.
The desire for running extremely large simulations has been a strong motivator for
supercomputing. Applications in nanotechnology, virtual reality, cryptography, signal
processing, mathematics, genomics, and simulation of transportation, societies, business,
physics, biology, and climate science will require petaflops performance in the next decade. It
is unlikely that this will be the end, and exaflop performance is already being discussed in the
supercomputing community. However, scaling up current architectures to the exascale may
be problematic, and may require new ways of thinking about how to manage complex
concurrent systems. These will to a large degree be shaped by the problems the computers
are intended to solve (and the relative ranking of these problems by society, affecting

28

funding) as well as by tradeoffs between performance with price, energy requirements3, andother constraints. The related area of very large‐scale information storage is also a key issue
for WBE.
An obvious related technology is the creation of virtual body models for use in medicine.
They can be used as training and study objects, or, at a more advanced stage, as personal
medical models. Such models might enable doctors to investigate the current state of thepatient, compare it to previous data, test or optimise various forms of simulated treatment,
train particular surgical approaches, etc. This is a technology of significant practicalimportance that is driven by current advances in medical imaging, the personalisation ofhealthcare, and improving physiological models. While most current models are either static
datasets or high‐level physiological models, personalisation requires developing methods ofphysiological parameter estimation from the data. Simulation will promote the developmentof more realistic body models usable for WBE. Conversely, the WBE focus on data‐driven
bottom‐up simulation appears to fit in with developing personalised biochemical and tissue
models.
Virtual test animals, if they could be developed to a sufficient degree of realism, would behigh in demand as faster and less expensive testing grounds for biomedical hypotheses(Michelson and Cole, 2007; Zheng, Kreuwel et al., 2007). They may perhaps also be a way of
avoiding the ethical controversies surrounding animal testing (although it is notinconceivable that concerns about animal welfare would in time be extended to emulated
animals). This could provide an impetus for the development of organism emulation
techniques and especially validation methods that help guarantee that the virtual animals
have the same properties as real animals would.
Overall, there is increasing interest and capability in quantitative modelling of biology (DiVentura, Lemerle et al., 2006). While much effort has gone into metabolic or control networksof particular interest, there is a push towards cell models encompassing metabolism, thegenome and proteomics (Ortoleva, Berry et al., 2003; Tomita, 2001; Schaff, Slepchenko et al.,
2001; Tyson, 2001). There is also interest in building simulators for modelling self‐assembly insubcellular systems (Ortoleva, Berry et al., 2003). Besides predicting biological responses andhelping to understand biology, modelling will likely become important for synthetic biology(Serrano, 2007).
In order to produce realistic virtual stimuli for a brain emulation, accurate simulations of thesensory input to the brain are needed. The same techniques used to investigate neuralfunction can be applied to the senses. It has also been proposed that technology to record
normal neural activity along the brain nerves and spinal cord could be helpful. Such data, forexample recorded using massively parallel electrode arrays or nanoimplants, would provide
good test data to validate a body model. Recorded sensory data would also be repeatable,
enabling comparisons between different variants of the same emulation. Neural interfacing is
also useful for developing better robotic body models. Currently, most interest in neuralinterfaces is focused on helping people with disabilities use sensory or motoric prosthetics.
While neural interfacing for enhancement purposes is possible, it is unlikely to become asignificant driver until prosthetic systems have become cheap, safe, and very effective.

3

An exascale computer using 2008 technology would require tens of megawatts (Sandia National Laboratories, 2008).

29

Issues
Emulation systems
A functioning brain emulation will include, in
addition to the brain model (the main part),
some way for the brain model to experiencebodily interactions with an environment. There
are two different ways in which this could beaccomplished: via a simulated virtual body
inhabiting a virtual reality (which can be linked
to the outside world); or via a hardware bodyconnected to the brain model via a bodyinterface module.
Entry‐level WBE does not require the capacity toaccommodate all of the original sensory
modalities or to provide a fully naturalistic body
experience. Simulated bodies and worlds, or
hardware body interfaces, as well as
Figure 9: Emulation system for completely
communications with the outside world, are not virtual emulation.
necessary per se for brain emulation except
insofar they are needed to maintain short‐term function of the brain. For long‐term function,
especially of human mind emulations, embodiment and communication are important.
Sensory or motor deprivation appears to produce intellectual and perceptual deficits within a
few days time (Zubek and Macneill, 1966).
The brain emulator performs the actual
emulation of the brain and closely linked
subsystems such as brain chemistry. The resultof its function is a series of states of emulated
brain activity. The emulation produces andreceives neural signals corresponding to motor
actions and sensory information (in addition,some body state information such as glucose
levels may be included).
The body simulator contains a model of the
body and its internal state. It produces sensory
signals based on the state of the body modeland the environment, sending them to thebrain emulation. It converts motor signals tomuscle contractions or direct movements in the
body model. The degree to which different
parts of the body require accurate simulation is
likely variable.

Figure 10: Emulation system for embodied
emulations.

The environment simulator maintains a model of the surrounding environment, responding
to actions from the body model and sending back simulated sensory information. This is also30

the most convenient point of interaction with the outside world. External information can beprojected into the environment model, virtual objects with real world affordances can be used
to trigger suitable interaction etc.
The overall emulation software system (the “exoself” to borrow Greg Egan’s term) would
regulate the function of the simulators and emulator, allocate computational resources, collectdiagnostic information, provide security (e.g. backups, firewalls, error detection, encryption)and so on. It could provide software services to emulated minds (accessed through the virtualenvironment) and/or outside experimenters.
A variant of the above system would be an embodied brain emulation, in which case thebody simulator would merely contain the translation functions between neural activity andphysical signals, and these would then be actuated using a hardware body. The body might
be completely artificial (in which case motor signals have to be mapped onto appropriate
body behaviours) or biological but equipped with nerve‐computer interfaces enabling
sensing and control. The computer system running the emulation does not have to bephysically present in the body.
It is certainly possible to introduce signals from the outside on higher levels than in asimulated or real body. It would be relatively trivial to add visual or auditory information
directly to the body model and have them appear as virtual or augmented reality.
Introducing signals directly into the brain emulation would require them to make sense asneural signals (e.g. brain stimulation or simulated drugs). “Virtual brain‐computer interfaces”
with perfect clarity and no risk of side effects could be implemented as extensions of the bodysimulation/interface.
If computing power turns out to be a bottleneck resource, then early emulations are likely to
run more slowly than the biological system they aim at emulating. This would limit theability of the emulations to interact in real‐time with the physical world. In distributed
emulations delays between computing nodes put a strong limit on how fast they can become.
The shortest delay using 100 m/s axons across the brain is about 1 ms. Assuming light speed
communication, processing nodes cannot be further away than 300 km if longer delays are to
be avoided.

Complications and exotica
Beside straight neural transmission through synapses there may be numerous other forms of
information processing in the brain that may have to be emulated. How important they arefor success in emulation remains uncertain. An important application of early brain
emulations and their precursors will be to enable testing of their influence.

Spinal cord
While traditionally the vertebrate spinal cord is often regarded as little more than a bundle ofmotor and sensor axons together with a central column of stereotypical reflex circuits andpattern generators, there is evidence that the processing may be more complex (Berg,
Alaburda et al., 2007) and that learning processes occur among spinal neurons (Crown,
Ferguson et al., 2002). The networks responsible for standing and stepping are extremely
flexible and unlikely to be hardwired (Cai, Courtine et al., 2006).

31

This means that emulating just the brain part of the central nervous system will lose muchbody control that has been learned and resides in the non‐scanned cord. On the other hand, it
is possible that a generic spinal cord network would, when attached to the emulated brain,
adapt (requiring only scanning and emulating one spinal cord, as well as finding a way of
attaching the spinal emulation to the brain emulation). But even if this is true, the time taken
may correspond to rehabilitation timescales of (subjective) months, during which time thesimulated body would be essentially paralysed. This might not be a major problem forpersonal identity in mind emulations (since people suffering spinal injuries do not lose
personal identity), but it would be a major limitation to their usefulness and might limit
development of animal models for brain emulation.
A similar concern could exist for other peripheral systems such as the retina and autonomic
nervous system ganglia.
The human spinal cord weighs 2.5% of the brain and contains around 10‐4 of the number ofneurons in the brain (13.5 million neurons). Hence adding the spinal cord to an emulation
would add a negligible extra scan and simulation load.

Synaptic adaptation
Synapses are usually characterized by their “strength”, the size of the postsynaptic potential
they produce in response to a given magnitude of incoming excitation. Many (most?)
synapses in the CNS also exhibit depression and/or facilitation: a temporary change in releaseprobability caused by repeated activity (Thomson, 2000). This rapid dynamics likely plays arole in a variety of brain functions, such as temporal filtering (Fortune and Rose, 2001 ),auditory processing (Macleod, Horiuchi et al., 2007) and motor control (Nadim and Manor,
2000). These changes occur on timescales longer than neural activity (tens of milliseconds) but
shorter than long‐term synaptic plasticity (minutes to hours). Adaptation has already been
included in numerous computational models. The computational load is usually 1‐3 extra
state variables in each synapse.

Unknown neurotransmitters and neuromodulators
Not all neuromodulators are known. At present about 10 major neurotransmitters and 200+
neuromodulators are known, and the number is increasing. (Thomas, 2006) lists 272
endogenous extracellular neuroactive signal transducers with known receptors, 2 gases, 19
substances with putative or unknown binding sites and 48 endogenous substances that may
or may not be neuroactive transducers (many of these may be more involved in generalbiochemical signalling than brain‐specific signals). Plotting the year of discovery for different
substances (or families of substances) suggests a linear or possibly sigmoidal growth overtime (Figure 11).

32

Ghrelin
Kisspeptin
OrexinNPFF
Nociceptin
Anandamide
Apelin
PACAP
NO
FGF
ANF
MCH
Galanin
Bombesin
GHRH
NPY
Dynorphin
PDML
VIP
Endorphin
CCK
Histamine
Neurotensin
Enkephalin
Somatostatin
Substance P
GnRH
TRH
GlycineDopamine
Angiotensin
Calcitonin
Serotonin
Glutamate
GABA
CRF
ACTH
Noradrenaline
Acetylcholine
1920

1930

1940

1950

1960

1970

1980

1990

2000

2010

Figure 11: Time of discovery of the neurotransmitter or neuromodulator activity of anumber of substances or families of substances. Data taken from (von Bohlen und Halbach
and Dermietzel 2006), likely underrepresenting the development after 2000.
An upper bound on the number of neuromodulators can be found using genomics. About 800
G‐protein coupled receptors can be found in the human genome, of which about half weresensory receptors. Many are “orphans” that lack known ligands, and methods of“deorphanizing” receptors by expressing them and determining what they bind to have beendeveloped. In the middle 1990’s about 150 receptors had been paired to 75 transmitters,leaving around 150‐200 orphans in 2003 (Wise, Jupe et al., 2004). At present, 7‐8 receptors aredeorphanized each year (von Bohlen und Halbach and Dermietzel, 2006); at this rate allorphans should be adopted within ≈20 years, leading to the discovery of around 50 moretransmitters (Civelli, 2005).
Similarly guanylyl cyclase‐coupled receptors (four orphans, (Wedel and Garbers, 1998)),
tyrosine kinase‐coupled receptors (<<100, (Muller‐Tidow, Schwable et al., 2004)) and cytokine
receptors would add a few extra transmitters.
However, there is room for some surprises. Recently it was found that protons were used tosignal in C. elegans rhythmic defecation (Pfeiffer, Johnson et al., 2008) mediated using a
Na+/H+ exchanger, and it is not inconceivable that similar mechanisms could exist in thebrain. Hence the upper bound on all transmitters may be set by not just receptors but also by
membrane transporter proteins.

33For WBE modelling all modulatory interactions is probably crucial, since we know thatneuromodulation does have important effects on mood, consciousness, learning andperception. This means not just detecting their existence but to create quantitative models ofthese interactions, a sizeable challenge for experimental and computational neuroscience.

Unknown ion channels
Similar to receptors, there are likely unknown ion channels that affect neuron dynamics.
The Ligand Gated Ion Channel Database currently contains 554 entries with 71 designated aschannel subunits from Homo sapiens (EMBL‐EBI, 2008; Donizelli, Djite et al., 2006). Voltage
gated ion channels form a superfamily with at least 143 genes (Yu, Yarov‐Yarovoy et al.,2005). This diversity is increased by multimerization (combinations of different subunits),
modifier subunits that do not form channels on their own but affect the function of channels
they are incorporated into, accessory proteins as well as alternate mRNA splicing and post‐translational modification (Gutman, Chandy et al., 2005). This would enable at least an orderof magnitude more variants.
Ion channel diversity increases the diversity of possible neuron electrophysiology, but notnecessarily in a linear manner. See the discussion of inferring electrophysiology from genetranscripts in the interpretation chapter.

Volume transmission
Surrounding the cells of the brain is the extracellular space, on average 200 Å across andcorresponding to 20% of brain volume (Nicholson, 2001). It transports nutrients and buffers
ions, but may also enable volume transmission of signalling molecules.
Volume transmission of small molecules appears fairly well established. Nitrous oxide ishydrophobic and has low molecular weight and can hence diffuse relatively freely throughmembranes: it can reach up to 0.1‐0.2 mm away from a release point under physiologicalconditions (Malinski, Taha et al., 1993; Schuman and Madison, 1994; Wood and Garthwaite,1994). While mainly believed to be important for autoregulation of blood supply, it may also
have a role in memory (Ledo, Frade et al., 2004). This might explain how LTP (Long Term
Potentiation) can induce “crosstalk” that reduces LTP induction thresholds over a span of 10
μm and ten minutes (Harvey and Svoboda, 2007).
Signal substances such as dopamine exhibit volume transmission (Rice, 2000) and this may
have effect for potentiation of nearby synapses during learning: simulations show that asingle synaptic release can be detected up to 20 μm away and with a 100 ms half‐life (Cragg,
Nicholson et al., 2001). Larger molecules have their relative diffusion speed reduced by thelimited geometry of the extracellular space, both in terms of its tortuosity and its anisotropy(Nicholson, 2001). As suggested by Robert Freitas, there may also exist active extracellulartransport modes. Diffusion rates are also affected by local flow of the CSF and can differ fromregion to region (Fenstermacher and Kaye, 1988); if this is relevant then local diffusion andflow measurements may be needed to develop at least a general brain diffusion model. The
geometric part of such data could be relatively easily gained from the high resolution 3Dscans needed for other WBE subproblems.

34

Rapid and broad volume transmission such as from nitrous oxide can be simulated using a
relatively coarse spatiotemporal grid size, while local transmission requires a grid with aspatial scale close to the neural scale if diffusion is severely hindered.
For constraining brain emulation it might be useful to analyse the expected diffusion anddetection distances of the ≈200 known chemical signalling molecules based on their molecularweight, diffusion constant and uptake (for different local neural geometries and source/sink
distributions). This would provide information on diffusion times that constrain the diffusionpart of the emulation and possibly show which chemical species need to be spatiallymodelled.

Body chemical environment
The body acts as an input/output unit that interacts with our perception and motor activity. It
also acts as a chemical environment that affects the brain through nutrients, hormones,
salinity, dissolved gases, and possibly immune signals. Most of these chemical signals occur
on a subconscious level and only become apparent when they influence e.g. hypothalamus to
produce hunger or thirst sensations. For brain emulation, some aspects of this chemical
environment has to be simulated.
This would require mapping the human metabolome, at least in regards to substances thatcross the blood‐brain barrier. The metabolome is likely on the order of 2,000‐2,500
compounds (Beecher, 2003; Wishart, Tzur et al., 2007) and largely does not change more
rapidly than on the second‐timescale. This suggests that compared to the demands of theWBE, the body chemistry model, while involved, would be relatively simple.
If a protein interaction model is needed rather than metabolism, then complexity increases.
According to one estimate the human interactome is around ≈650,000 protein‐protein
interactions (Stumpf, Thorne et al., 2008).

Neurogenesis and remodelling
Recent results show that neurogenesis persists in some brain regions in adulthood, and might
have nontrivial functional consequences (Saxe, Malleret et al., 2007). During neurite
outgrowth, and possibly afterwards, cell adhesion proteins can affect gene expression andpossible neuron function by affecting second messenger systems and calcium levels (Crossin
and Krushel, 2000). However, neurogenesis is mainly confined to discrete regions of the brain
and does not occur to a great extent in adult neocortex (Bhardwaj, Curtis et al., 2006).
Since neurogenesis occurs on fairly slow timescales (> 1 week) compared to brain activity andnormal plasticity, it could probably be ignored in brain emulation if the goal is an emulationthat is intended to function faithfully for only a few days and not to exhibit truly long‐term
memory consolidation or adaptation.
A related issue is remodelling of dendrites and synapses. Over the span of months dendrites
can grow, retract and add new branch tips in a cell type‐specific manner (Lee, Huang et al.,2006). Similarly synaptic spines in the adult brain can change within hours to days, although
the majority remain stable over multi‐month timespans (Grutzendler, Kasthuri et al., 2002;
Holtmaat, Trachtenberg et al., 2005; Zuo, Lin et al., 2005). Even if neurogenesis is ignored andthe emulation is of an adult brain, it is likely that such remodelling is important to learningand adaptation.

35

Simulating stem cell proliferation would require data structures representing different cells
and their differentiation status, data on what triggers neurogenesis, and models allowing forthe gradual integration of the cells into the network. Such a simulation would involve
modelling the geometry and mechanics of cells, possibly even tissue differentiation. Dendritic
and synaptic remodelling would also require a geometry and mechanics model. While
technically involved and requiring at least a geometry model for each dendritic compartment
the computational demands appear small compared to neural activity.

Glia cells
Glia cells have traditionally been regarded as merely supporting actors to the neurons, but
recent results suggest that they may play a fairly active role in neural activity. Beside theimportant role of myelinization for increasing neural transmission speed, at the very least
they have strong effects on the local chemical environment of the extracellular spacesurrounding neurons and synapses.
Glial cells exhibit calcium waves that spread along glial networks and affect nearby neurons(Newman and Zahs, 1998). They can both excite and inhibit nearby neurons throughneurotransmitters (Kozlov, Angulo et al., 2006). Conversely, the calcium concentration of glial
cells is affected by the presence of specific neuromodulators (Perea and Araque, 2005). Thissuggests that the glial cells acts as an information processing network integrated with theneurons (Fellin and Carmignoto, 2004). One role could be in regulating local energy andoxygen supply.
If glial processing turns out to be significant and fine‐grained, brain emulation would have toemulate the glia cells in the same way as neurons, increasing the storage demands by at least
one order of magnitude. However, the time constants for glial calcium dynamics is generally
far slower than the dynamics of action potentials (on the order of seconds or more),suggesting that the time resolution would not have to be as fine, making the computational
demands increase far less steeply.

Ephaptic effects
Electrical effects may also play a role via so called “ephaptic transmission”. In a highresistance environment, currents from action potentials are forced to flow throughneighbouring neurons, changing their excitability. It has been claimed that this process
constitutes a form of communication in the brain, in particular the hippocampus (Krnjevic,1986). However, in most parts of the brain there is a large extracellular space and blocking
myelin, so even if ephaptic interactions play a role, they do so only locally, e.g. in theolfactory system (Bokil, Laaris et al., 2001), dense demyelinated nerve bundles (Reutskiy,
Rossoni et al., 2003), or trigeminal pain syndromes (Love and Coakham, 2001). It should be
noted that the nervous system appears relatively insensitive to everyday external electricfields (Valberg, Kavet et al., 1997; Swanson and Kheifets, 2006).
If ephaptic effects were important, the emulation would need to take the locally induced
electromagnetic fields into account. This would plausibly involve dividing the extracellularspace (possibly also the intracellular space) into finite elements where the field can beassumed to be constant, linear or otherwise easily approximable. The cortical extracellular
length constant is on order of ≈100 μm (Gardner‐Medwin, 1983), which would necessitate on
the order of 1.4∙1012 such compartments if each compartment is 1/10 of the length constant.

36

Each compartment would need at least two vector state variables and 6 components of aconductivity tensor; assuming one byte for each, the total memory requirements would be on
the order of 10 terabytes. Compared to estimates of neural simulation complexity, this isrelatively manageable. The processing needed to update these compartments would be on thesame order as a detailed compartment model of every neuron and glia cell.

Dynamical state
The methods for creating the necessary data for brain emulation discussed in this paper deal
with just the physical structure of the brain tissue, not its state of activity. Some informationsuch as working memory may be stored just as ongoing patterns of neural excitation andwould be lost. Similarly, information in calcium concentrations, synaptic vesicle depletion,and diffusing neuromodulators may be lost during scanning. A likely consequence would beamnesia of the time closest to the scanning.
However, loss of brain activity does not seem to prevent the return of function and personal
identity. This is demonstrated by the reawakening of coma patients, and by cold water near‐drowning cases in which brain activity temporarily ceased due to hypothermia (Elixson,
1991).

Quantum computation
While practically all neuroscientists subscribe to the dogma that neural activity is aphenomenon that occurs on a classical scale, there have been proposals (mainly fromphysicists) that quantum effects play an important role in the function of the brain (Penrose,
1989; Hameroff, 1987). So far there is no evidence for quantum effects in the brain beyond
quantum chemistry, and no evidence that such effects play an important role for intelligenceor consciousness (Litt, Eliasmith et al., 2006). There is no lack of possible computational
primitives in neurobiology nor any phenomena that appear unexplainable in terms ofclassical computations (Koch and Hepp, 2006). Quantitative estimates for decoherence times
for ions during action potentials and microtubules suggest that they decohere on a timescaleof 10‐20 – 10‐13 s, about ten orders of magnitude faster than the normal neural activity
timescales. Hence quantum effects are unlikely to persist long enough to affect processing(Tegmark, 2000). This, however, has not deterred supporters of quantum consciousness, whoargue that there may be mechanisms protecting quantum superpositions over significant
periods (Rosa and Faber, 2004; Hagan, Hameroff et al., 2002).
If these quantum‐mind hypotheses were true, brain emulation would be significantly more
complex, but not impossible given the right (quantum) computer. In (Hameroff, 1987) mind
emulation is considered based on quantum cellular automata, which in turn are based on the
microtubule network that the author suggests underlies consciousness.
Assuming 7.1 microtubules per square μm and 768.9 μm in average length (Cash, Aliev et al.,2003) and that 1/30 of brain volume is neurons (although given that micotubuli networks
occurs in all cells, glia – and any other cell type! – may count too) gives 1016 microtubules. If
each stores just a single quantum bit this would correspond to a 1016 qubit system, requiring aphysically intractable 210^16 bit classical computer to emulate. If only the microtubules inside acell act as a quantum computing network, the emulation would have to include 1011
connected 130,000 qubit quantum computers. Another calculation, assuming merely classicalcomputation in microtubules, suggests 1019 bytes per brain operating at 1028 FLOPS(Tuszynski, 2006). One problem with these calculations is that they impute such a profoundly37

large computational capacity at a subneural level that a macroscopic brain seems unnecessary(especially since neurons are metabolically costly).

Analog computation
A surprisingly common doubt expressed about the possibility of simulating even simple
neural systems is that they are analog rather than digital. The doubt is based on theassumption that there is an important qualitative difference between continuous and discretevariables.
If computations in the brain make use of the full power of continuous variables the brain may
essentially be able to achieve “hypercomputation”, enabling it to calculate things an ordinary
Turing machine cannot (Ord, 2006; Siegelmann and Sontag, 1995). See (Zenil and Hernandez‐
Quiroz, 2007) for a review of how different brain computational architectures would enabledifferent levels of computational power, and the requirements for neural networks tosimulate these.
However, brains are made of imperfect structures which are, in turn, made of discrete atoms
obeying quantum mechanical rules forcing them into discrete energy states, possibly also
limited by a space‐time that is discrete on the Planck scale (as well as noise, see below) and so
it is unlikely that the high precision required of hypercomputation can be physically realized(Eliasmith, 2001). Even if hypercomputation were physically possible, it would by no means
be certain that it is used in the brain, and it might even be difficult to detect if it were (thecontinual and otherwise hard to explain failure of WBE would be some evidence in thisdirection). However, finding clear examples of non‐Turing computable abilities of the mind
would be a way of ruling out Turing emulation.
A discrete approximation of an analog system can be made arbitrarily exact by refining theresolution. If an M bit value is used to represent a continuous signal, the signal‐to‐noise ratio
is approximately 20 log10(2M) dB (assuming uniform distribution of discretization errors,
which is likely for large M). This can relatively easily be made smaller than the natural noise
sources such as unreliable synapses, thermal, or electrical noise. The thermal noise is on theorder of 4.2∙10‐21 J, which suggests that energy differences smaller than this can be ignored
unless they occur in isolated subsystems or on timescales fast enough to not thermalize. Field
potential recordings commonly have fluctuations on the order of millivolts due to neuron
firing and a background noise on the order of tens of microvolts. Again this suggests a limit
to the necessary precision of simulation variables4.

Determinism
A somewhat related criticism is the assumed determinism of computers, while the brain isassumed either to contain true randomness or a physically indeterministic element (often
declared to be “free will”).
The randomness version of the determinism criticism can be met by including sufficient noisein the simulation. Random events may play a role in the function of the nervous system. In
particular, the number of individual molecules involved in transcription regulation of some
proteins and in the function of synaptic spines is low enough that individual random
interactions can affect whether a gene is switched on or whether a phosphorylation cascade
4

Analog computation may still be a useful hardware paradigm under some conditions.

38

happens. This randomness may have biological importance, and is sometimes included inbiophysical models. Another possible role of noise might be stochastic resonance, where noise
acts to increase the signal‐to‐noise ratio in nonlinear (and information‐theoreticallysuboptimal) systems attempting to detect a weak periodic input (Gammaitoni, Hanggi et al.,1998). It has been demonstrated in mechanoreceptors (Douglass and Wilkens, 1998) andvarious other forms of sensory systems (Douglass and Wilkens, 1998) as a way of detecting
faint signals. However, while the exact spectral distribution and power may matter for an
emulation, it does not seem that the resonance effect would be disrupted by using
pseudorandom noise if the recurrence time is long enough. Unless there are some important“hidden variables” in the noise of the brain, noise can be easily approximated using a suitably
long‐periodic random number generator (Tegmark, 2000) or even by means of an attached
physical random number generator using quantum mechanics (Stefanov, Gisin et al., 2000).
Randomness is therefore highly unlikely to pose a major obstacle to WBE.
Hidden variables or indeterministic free will appear to have the same status as quantum
consciousness: while not in any obvious way directly ruled out by current observations, there
is no evidence that they occur or are necessary to explain observed phenomena.

Summary
Table 4 shows an overview of informal estimates of the likeliehood that certain features areneeded for WBE and whether they would pose serious implementation problems if needed.
Table 4: Likeliehood estimates of modelling complications.
Spinal cord

Likeliehood needed for WBE
LikelySynaptic adaptation

Very likely

Currently unknown
neurotransmitters andneuromodulators
Currently unknown ion channels

Very likely

Very likely

Volume transmission

Somewhat likely

Body chemical environment

Somewhat likely

Neurogenesis and remodelling

Somewhat likely

Glia cells

Possible

Ephaptic effects

Possible

Dynamical state

Very unlikely

Quantum computation

Very unlikely

Analog computation

Very unlikely

True randomness

Very unlikely

Implementation problems
Minor. Would require scanningsome extra neural tissue.
Minor. Introduces extra state‐variables and parameters that need
to be set.
Minor. Similar to known
transmitters and modulators.
Minor. Similar to known ion
channels.
Medium. Requires diffusion models
and microscale geometry.
Medium. Requires metabolomic
models and data.
Medium. Requires cell mechanics
and growth models.
Minor. Would require more
simulation compartments, but likely
running on a slower timescale.
Medium. Would require relatively
fine‐grained EM simulation.
Profound. Would preclude most
proposed scanning methods.
Profound. Would preclude currently
conceived scanning methods andwould require quantum computing.
Profound. Would require analog
computer hardware.
Medium to profound, depending onwhether merely “true” random noiseor “hidden variables” are needed.

39

Scanning
The first step in brain emulation is to acquire the necessary information from a physical brain.
We will call this step “scanning”.
Brain emulation for compartment models of neuron activity needs to acquire both geometricdata about the localization and morphology of the nervous connections andfunctional/chemical data about their nature such as what ion channels, receptors, andneurotransmitters are present, the presence of electrical synapses, electrical membrane
properties, phosphorylation states of synapses, and genetic expression states. This must be
done at a sufficient resolution. It may be possible to infer some functional properties such aswhether a synapse is excitatory or inhibitory purely from geometry (e.g., a synapse from asmooth neuron with a particular morphology is likely inhibitory). Yet it remains unclear how
much information about synaptic strength and neuromodulator type can be inferred frompure geometry at a given level of resolution.
There are several potential approaches to scanning. Here we focus on destructive scanning, inwhich the brain is destructively disassembled during the emulation process. The process
could be applied immediately after death or on cryogenically preserved brain tissue. This is
the technologically simplest approach. Destructive scanning has greater freedom both inphysical effects used, energy levels, and fixating the brain through freezing and/or chemical
fixation. Possible alternatives include non‐destructive scanning and gradual replacement;these are discussed in Appendix E.
Most methods discussed in this section require the sectioning of the brain into smaller pieces
that can be accurately scanned. The sectioning methods and the handling of the tissue may
pose significant problems and require the development of special handling technology. In
particular, sample distortion and drift are key problems; the first requiring very careful
sectioning methods, the second either high‐precision equipment or embedding of fiducial
markers.

MRI microscopy
Although MRI imaging may not be suitable for scanning entire brains at sufficient resolution,
MRI microscopy might be suitable for scanning parts of them if water diffusion is stopped byfreezing or fixation.
3D MR microscopy has achieved resolutions on the order of 3.7×3.3×3.3 μm (Ciobanu, Seeber
et al., 2002) but is limited by the small field of view and long exposure times. One way of
improving the field of view may be parallelism, where an array of MRI coils is moved overthe surface (McDougall and Wright, 2007).
A combination of MRI and AFM is magnetic resonance force microscopy (MRFM) where amagnetic bead is placed on an ultra thin cantilever and moved over the sample (or thereverse). By generating RF magnetic fields the spins of nuclei within the resonant slice
beneath the bead can be flipped, producing forces on the cantilever that can be detected. This
would enable identification of the molecules present near the surface. Current resolutions
achieved are 80 nm voxels in a scan volume of 0.5 μm3 (Chao, Dougherty et al., 2004) andsingle spin detection with 25 nm resolution in one dimension (Rugar, Budakian et al., 2004).

40

Whether this can be scaled up to detecting e.g. the presence of cell membranes or particular
neurotransmitters remains to be seen.

Optical methods

Figure 12: Confocal microscopy picture of visual cortex neurons. Stained with GABA‐
channel antibody (red) and containing neurons expressing green fluorescent protein. The
white scale bar is 100 μm. (Lee, Huang et al., 2006).
Optical microscopy methods are limited by the need for staining tissues to make relevant
details stand out and the diffraction limits set by the wavelength of light (≈0.2 μm). The main
benefit is that they go well together with various spectrometric methods (see below) for
determining the composition of tissues.
Sub‐diffraction optical microscopy is possible, if limited. Various fluorescence‐based methods
have been developed that could be applicable if fluorophores could be attached to the brain
tissue in a way that provided the relevant information. Structured illumination techniques
use patterned illumination and post‐collection analysis of the interference fringes between theillumination and sample image together with optical nonlinearities to break the diffraction
limit. This way, 50 nm resolving power can be achieved in a wide field, at the price ofphotodamage due to the high power levels (Gustafsson, 2005). Near‐field scanning optical
microscopy (NSOM) uses a multinanometre optic fiber to scan the substrate using near‐field
optics, gaining resolution (down to the multi‐nanometer scale) and freedom from using

41

fluorescent markers at the expense of speed and depth of field. It can also be extended intonear field spectroscopy.
Confocal microscopy suffers from having to scan through the entire region of interest andquality degrades away from the focal plane. Using inverse scattering methods depth‐
independent focus can be achieved (Ralston, Marks et al., 2007).
Optical histology and dissection
All‐optical histology uses femtosecond laser pulses to ablate tissue samples, avoiding theneed for mechanical removal of the surface layer (Tsai, Friedman et al., 2003). This treatment
appears to change the tissue 2‐10 μm from the surface. However, Tsai et al. were optimistic
about being able to scan a fluorescence labelled entire mouse brain into 2 terapixels at thediffraction limit of spatial resolution.
Another interesting application of femtosecond laser pulses is microdissection (Sakakura,
Kajiyama et al., 2007; Colombelli, Grill et al., 2004). The laser was able to remove 100 μm
samples from plant and animal material, modifying a ~10 μm border. This form of opticaldissection might be an important complement for EM methods, in that, after scanning thegeometry of the tissue at a high resolution, relevant pieces can be removed and analyzed
microchemically. This could enable gaining both the EM connectivity data and detailed
biochemistry information. Platforms already exist that can both inject biomolecules into
individual cells, perform microdissection, isolate and collect individual cells using laser
catapulting, and set up complex optical force patterns (Stuhrmann, Jahnke et al., 2006).
Knife‐Edge Scanning Microscopy (KESM)

Figure 13: Knife Edge‐Scanning Microscope. Microscope objective (left), diamond knife(right) cutting the specimen in the center. Note the illumination refracted through theknife (Copyright Brain Networks Laboratory, Texas A&M University).

42

KESM is a method for staining and imaging large volumes at high resolutions by integrating
the sectioning and imaging step. It was developed for reconstruction and modelling of thethree‐dimensional anatomical structure of individual cells in situ. While having lower
resolution than SBF‐SEM (see below) it enables the imaging of an entire macroscopic tissue
volume such as a mouse brain in reasonable time (McCormick, 2002a; McCormick, 2002b).
The KESM acts simultaneously as a microtome and a microscope. A diamond knife is used
for the cutting and illumination, moving together with the microscope across a stationary
tissue specimen embedded in plastic. Imaging occurs of the just‐separated section along a 1D
strip. Imaging at 0.3 μm resolution of 0.5 μm thick sections has been demonstrated,
producing datasets where microvasculature and individual cells can be traced using
contouring algorithms. The instrument scans at rates up to 200Mpixels/s. This would enablescanning a mouse brain (≈1 cm3) in a hundred hours, giving a 15 terabyte dataset of
uncompressed data (Mayerich, Abbott et al., 2008). The width of field is 2.5 mm for 64 μm
resolution and 0.625 mm for 32 μm resolution (McCormick, Koh et al., 2004). In order toreduce jittering and using knives larger than the microscope field of view, the sample is cut ina stair‐step manner (Koh and McCormick, 2003).
The main limitation is the need for staining inside a volume. At present the number of suchstains is limited. Using transgenic animals expressing stains or fluorescence could enablemapping of different neural systems.

X‐Ray microscopy

Figure 14: X‐ray microtomography stereo image of the supraesophageal ganglion in
Drosophilia, showing individual neurons. Scale 50 μm. (Mizutani, Takeuchi et al., 2007)
X‐ray microscopy is intermediate between optical microscopy and electron microscopy interms of resolution.
Hard X‐ray microtomography has been used to image the larval Drosophila brain (Mizutani,
Takeuchi et al., 2007). This was achieved by staining neurons with metal and then acquiring
images rotated by 0.12˚ with 300 ms exposure, with a total acquisition time of 1800 seconds.
The field of view was on the order of 0.5 mm2 with a pixel resolution of 0.47x0.47 μm; theresulting tomographical resolution was about 1 μm in each direction. Individual neurons

43could be identified, and the authors estimate that the setup could in principle scan a 1 mm3block of mammalian nerve tissue with 103‐104 neurons. However, the resolution is likely too
crude to identify synapses and connectivity.
By using soft X‐rays with wavelengths in the “water window” (between the absorption edgesof oxygen and carbon at 2.3‐4.4 nm) where carbon absorbs ten times as strongly as water,organic structures can be imaged with good contrast down to at least 30 nm resolution.
Advantages include that it can be done without special sample preparation procedures andcan be applied to hydrated cells, it permits thicker specimens than EM (up to 10 μm), X‐ray
absorption spectra of localized regions can be recorded by using different X‐ray energies, andpotentially three‐dimensional imaging could be possible using multiple beams (Yamamotoand Shinohara, 2002).
A major disadvantage is that the X‐rays cause tissue damage. When the total X‐ray flux goes
above 4∙105 photons per μm2, myofibrils lose their contractility; and above 106 photons per
μm2, yeast cells lose their dye exclusion ability (a sign that they are dead or punctured). Thisis lower than the fluxes of X‐ray microscopes with 50 nm resolution, making scanning oftissue while retaining its function impossible (Fujisaki, Takahashi et al., 1996). One way of
avoiding degradation of the image is to use a rapid pulse of rays. This also avoids motion
blurring due to diffusion in the case of hydrated tissue: to image freely diffusing objects ofsize 10 nm exposure shorter than 0.1 ms is needed, while a cellular organelle would require
between 14 ms – 1.4 s depending on size (Ito and Shinohara, 1992). Using vitrified tissue theradiation dose can be increased thousandfold without structural changes, and the diffusion
issue disappears (Methe, Spring et al., 1997). These concerns make X‐ray imaging unlikely towork for non‐destructive scanning.
Of particular interest for WBE is the possibility of doing spectromicrosopy to determine thelocal chemical environment. X‐ray absorption edges are affected by the chemical binding stateof the atom probed. Different amino acids have distinguishable ‘fingerprints’ that are
relatively unaffected by peptide bonding. In principle, the spectra of proteins could be
predicted based on their sequences. Scanning X‐ray microscopes have long exposure times(minutes), although they deposit 5‐10 times less radiation in the sample (Jacobsen, 1999).
While currently far too slow for WBE purposes, finding ways of speeding up this process
may make it relevant for WBE.

Atomic‐beam microscopy
Instead of photons or electrons, neutral atoms could be used to image a sample. Since the de
Broglie wavelength of thermal atoms is subnanometer, the resolution could be very high. By
using uncharged and inert atoms like helium, the beam would also be non‐destructive (Holstand Allison, 1997). Helium atom scattering has a large cross‐section with hydrogen, whichmight make it possible to detect membranes in unstained tissue.
At present, imaging at high resolution using atomic beam microscopy has not been achieved(lower resolution has been achieved (Doak, Grisenti et al., 1999)). Development of ridged
atomic mirrors has enabled focusing neutral atom beams (Oberst, Kouznetsov et al., 2005;
Shimizu and Fujita, 2002) and would in principle allow focusing the beam to a spot size oftens of nanometers (Kouznetsov, Oberst et al., 2006); by scanning the spot across the sample
an image could be built up like in other forms of scanning microscopy.

44Electron microscopy

Figure 15: EM picture of rat hippocampus neuropil. D marks a dendrite of a pyramidal cell.
Several synapses can be seen to the left, noticeable by the presence of small sphericalvesicles on the presynaptic side and a dark postsynaptic density on the receiving side. The
scale bar is 1 μm. (Copyright J Spacek, Synapse Web)Electron microscopy can resolve the fine details of axons and dendrites in dense neural tissue.
Images can be created through transmission electron microscopy (TEM), where electrons aresent through tissue, or scanning electron microscopy (SEM) where electrons are scatteredfrom the surface: both methods require fixing the sample by freezing and/or embedding it inpolymer. TEM has achieved 0.1 nm imaging (Nellist, Chisholm et al., 2004). However, themain challenge is to automate sectioning and acquisition of data. The three current main
methods are serial section electron tomography (SSET), serial section transmission electron
microscopy (SSTEM) and serial block‐face scanning electron microscopy (SBFSEM)(Briggman and Denk, 2006). Two new methods that may be useful are FEI’s DualBeam
Focused Ion Beam SEM (FIBSEM) and automatic ultrathin sectioning and SEM imaging(ATLUM).
SSET: High resolution TEM 3D images can be created using tilt‐series‐based tomography
where the preparation is tilted relative to the electron beam, enabling the recording of depth
information (Frank, 1992; Penczek, Marko et al., 1995). This method appears suited mainly for
local scanning (such as imaging cellular organelles) and cannot penetrate very deep into thesurface (around 1 μm) (Lučić, Förster et al., 2005).
SSTEM: Creating ultrathin slices for TEM is another possibility. (Tsang, 2005) created a three‐dimensional model of the neuromuscular junction through serial TEM of 50 nm sections
created using an ultramicrotome. (White, Southgate et al., 1986) used serial sections to45

reconstruct the C. elegans nervous system. However, sectioning is physically tricky and labor
intensive.
SBFSEM: One way of reducing the problems of sectioning is to place the microtome inside
the microscope chamber (Leighton, 1981); for further contrast, plasma etching was used
(Kuzirian and Leighton, 1983). (Denk and Horstmann, 2004) demonstrated thatbackscattering contrast could be used instead in a SEM, simplifying the technique. They
produced stacks of 50‐70 nm thick sections using an automated microtome in the microscopechamber, with lateral jitter less than 10 nm. The resolution and field size was limited by thecommercially available system. They estimated that tracing of axons with 20 nm resolution
and S/N ratio of about 10 within a 200 μm cube could take about a day (while 10 nm x 10 nm
x 50 nm voxels at S/N 100 would require a scan time on the order of a year).
Reconstructing volumes from ultrathin sections faces many practical challenges. Current
electron microscopes cannot handle sections wider than 1‐2 mm. Long series of sections areneeded but the risk of errors or damage increase with the length, and the number of specimen
holding grids becomes excessive (unless sectioning occurs inside the microscope (Kuzirian
and Leighton, 1983)). Current state of the art for practical reconstruction from tissue blocks isabout 0.1 mm3 , containing about 107‐108 synapses (Fiala, 2002).
FIBSEM5: The semiconductor industry has long used focused ion beams (FIB) (usually
accelerated beams of gallium ions) to very precisely cutaway parts of integrated circuit chips
for failure analysis. Researchers at FEI have recently demonstrated that this technique andinstrument can also be applied to plastic embedded neural tissue in a manner similar to the
SBFSEM above (Mulders, Knott et al., 2006). In the FIBSEM, the top 30‐50 nm layer of a blockof tissue (having block face dimensions of approximately 100 x 100 μm) is ablated away using
the focused ion beam. The resulting block face is then imaged with the SEM using thebackscatter signal just as is done in the SBFSEM, and this process is repeated to image
hundreds of successive layers.
Because the FIBSEM ablates away the block’s top layer using an ion beam (instead of adiamond knife), the ‘cooking’ of the block surface that occurs in SBFSEM due to the highbeam current is not a problem. Using longer integration times, FIBSEM images havedemonstrated lateral resolutions of 5nm or better and much improved signal to noise ratios.
ATLUM: The above SSET and SSTEM approaches use sections obtained by diamond knife
sectioning on a traditional semi‐automated ultramicrotome. This process is only semi‐
automated because the collection of these sections requires manually scooping free‐floating
sections from the knife’s water boat onto TEM slot grids using an eyelash. This is an
inherently unreliable process for all but the smallest volumes.
The Automatic Tape‐Collecting Lathe Ultramicrotome (ATLUM) is a prototype machine
recently developed at Harvard University that fully automates the process of ultrathin
sectioning and collection (Hayworth, Kashturi et al., 2006; Hayworth, 2007). In the ATLUM atissue sample (typically 1‐2mm in width, 10mm long, and 0.5mm deep) is mounted on a steel
axle and is rotated continuously via an ultraprecise airbearing while a piezo‐driven diamondknife slices off one ultrathin section per revolution. A feedback loop employing capacitive
sensors maintains the position of the knife relative to the axle to within approximately 10nm
allowing even large area tissue sections (many square millimeters) to be cut to thicknesses
5

Text derived from Kenneth Hayworth.

46

below 40nm. Each section comes streaming off the knife’s edge onto the surface of a waterboat attached behind the knife. In traditional ultramicrotomy this fragile floating section
would have to be collected manually (via dexterous use of an eyelash) onto a TEM slot grid, anotoriously unreliable process. In contrast, in the ATLUM each section is immediately
collected by the mechanism onto a submerged conveyor belt of carbon coated Mylar tape (100μm thick and 8 mm wide). In this way, hundreds of large‐area ultrathin sections areautomatically secured onto a meters‐long Mylar tape.

Figure 16: Overview of ATLUM operation. (Copyright Lichtman Lab, Harvard University)
This tape is subsequently stained with heavy metals and imaged in a SEM. Imaging via theSEM backscatter signal (like the SBFSEM and FIBSEM approaches above) allows the ATLUM
to dispense with fragile TEM slot grids (which also removes their width‐of‐section
limitation). The Mylar tape provides a sturdy substrate for handling and storage while its
carbon coating prevents charging and beam damage during SEM imaging.
Images obtained from ATLUM tissue tapes are of equivalent quality to traditional TEM
images showing lateral resolution better than 5 nanometers. Recent tests have also shown thata tomographic tilt series (as in the SSET technique) may be performed on ATLUM collected
sections to obtain additional depth information. This is performed by tilting the section at
various angles with respect to the SEM’s electron beam thus providing Z‐resolutions evenfiner than the section thickness itself. Because the sections are stored for later imaging they
can be post‐stained with heavy metals (producing images with superior signal to noise ratios
and much faster imaging times than the SBFSEM and FIBSEM block face imagers) and, if
needed, they can be poststained with a series of other stains for overlaying chemical analysis
maps on the high resolution SEM structural images.

47

Comparison of techniques
Resolution
With the exception of the SBFSEM, all of these techniques have already demonstrated
sufficient resolution to map the exact point‐to‐point connectivity of brain tissue down to thelevel of counting synaptic vesicles in individual synapses. With additional modifications it
may be possible for the SBFSEM to also reach these resolutions.
Reliability and robustness
Transmission EM techniques like the SSTEM and SSET require sections to be mounted on slot
grids. The process of collecting sections on slot grids has not yet been successfully automated,and sections mounted on slot grids are themselves extremely fragile (because they are
essentially freely supported structures less than a micron thick). This raises serious doubts as
to the feasibility of scaling these techniques up to very large volumes of neural tissue(although machines like the ATLUM may be modifiable to allow automated collection ofsections on tapes with prefabricated slots, or alternatively slots can be created in the tape aftercollection is completed). SEM backscatter imaging seems to offer equivalent resolution andimage quality while avoiding these pitfalls intrinsic to TEM approaches.
One remaining possible advantage of TEM approaches is imaging speed. Images in current
SEMs are built up one pixel at a time as the electron beam scans across the sample’s surface.
In contrast, the pixels in TEM images are captured in parallel by a CCD + scintillator mounted
below the section. This fact could, in principle, allow imaging times orders of magnitude
faster than SEM imaging. However, many technical issues complicate this comparison. In
practice, current TEMs image sections only slightly faster than SEMs do. In addition,
multibeam SEMs (described below) are currently being developed that will massively
parallelize, and thus speed up, SEM image acquisition
Block face approaches (SBFSEM and FIBSEM) have inherent reliability since they avoid theperilous step of collecting ultrathin sections by simply destroying them in situ after having
already imaged them. The FIBSEM technique is additionally, at least in principle, robust to
small differences in embedding quality. All of the other techniques use diamond knife
sectioning which requires good uniformity of resin infiltration and resin hardness throughout
the tissue block to section smoothly. Of course tissue samples for all the techniques must be
infiltrated correctly for proper ultrastructural preservation
While the ATLUM is necessarily less reliable than block face approaches (since it collects
ultrathin sections for later imaging) it still has the potential for extreme reliability over large
volumes. This is because the ultrathin sections, within the ATLUM mechanism, are secured tothe sturdy Mylar tape almost immediately after they are sectioned by the diamond knife. In
fact, the leading edge of each section is secured to the collection tape while the trail edge isstill being sectioned by the knife. In this way each section is always under complete control ofthe mechanism.
Imaging time
For concreteness, let’s outline a reasonable near‐term neuronal circuit mapping goal
necessary to support a set of “partial” brain circuit emulation experiments. We can thencompare the imaging times, and thus the feasibility, of the different methods. One of the mostinteresting and best studied pieces of brain circuitry is that underlying orientation tuning in
the primary visual cortex (V1) in the cat and in the primate. This circuit’s crude functional
properties (orientation tuned ‘simple’ and ‘complex’ cells) have been known for decades yet
the circuitry underlying these functional responses remains a topic of heated debate48generating dozens of journal articles every year. More importantly, our lack of a detailed
understanding of the neuronal circuitry has blocked progress into V1’s more subtle
computational properties such as its role in perceptual grouping.
A trial brain emulation experiment might set as a goal the modelling of orientation tuning
responses of a few cells in V1 (possibly correlated with previous two‐photon recording ofactivity within the same tissue). This task would not require entire volumes of complex
neuropil be traced in total. It would require, however, imaging the connectivity of at least
several dozen neurons in the various layers of V1, and their connectivity both with each otherand with many dozen axonal projections to V1 from the lateral geniculate nucleus (LGN). In
addition, these axonal projections from the LGN would need to be traced back to their origins
to determine their relative positions and thus to infer their own receptive field properties. In
whole, such a study would require nanoresolution tracing of parts of over one hundredneurons and thousands of synaptic connections spanning a volume on the order of 10mm3(This is assuming that the myelinated projections between LGN and V1 can be traced at much
lower resolution, excluding it from the volume estimate). Because of the nature of the study,
such neurons could not be selectively labelled beforehand and so the electron microscopic
methods described in this section (which can image any arbitrary neuronal cell or process) are
the only current techniques which may be capable of performing the task.
SSET and SSTEM: As discussed above, the manual collection of TEM‐ready sections (fortechniques like the SSET and SSTEM) is out of the question for volumes as large as 10mm3.
SBFSEM and FIBSEM: So far the largest volumes sectioned in these devices have been lessthan 0.01 mm3, with blockface dimensions typically 200 x 200 μm or less. Imaging rate onthese devices is around 100kHz (10 microseconds per pixel). In general, blockface imaging
approaches necessitate such slow imaging rates (to obtain adequate signal to noise ratios)
since the material can only be lightly stained while in block form.
Our V1 circuit tracing scenario does not strictly require the entire 10mm3 volume be imaged
at highest resolution; however, as the neuronal circuits to be traced wander randomly
throughout the entire volume, one does not know a priori which parts to image at highresolution. Because, in these blockface imaging devices, each section is destroyed in situ,
there is only one chance to image it and thus in effect all parts of the volume must be imaged
at sufficient resolution to trace the finest neuronal processes. Assuming 5 nm lateral
resolution and 50 nm sections (typical values for neuropil tracing studies) the 10 mm3 block
consists of 8∙1015 voxels and would require on the order of 2,000 years to image! Such long
imaging time makes this tracing study infeasible on these machines.
ATLUM: The largest volume sectioned on the ATLUM so far is 0.1mm3 with blockface
dimensions of 1.4mm x 4.5mm and section thickness of 45nm (total of 400 sections). ATLUM
cutting speed is around 0.03mm/sec and each of these ultrathin sections (6.3mm2 in area) was
produced at a rate of approximately 4 minutes per section. If such a rate could be reliably
sustained it would take about 3 months for an ATLUM‐like device to reduce a 10mm3 tissue
block to a series of 50nm sections each securely mounted on a long Mylar tape ready for
imaging.
Because the ATLUM collects sections for later imaging, its sections can be poststained withheavy metals. This allows adequate signal to noise ratios with shorter image acquisition
times. Imaging rate is around 1MHz (1.0 microsecond per pixel) so bulk imaging the entire
10mm3 would require on the order of 200 years. This still would render the project infeasible;

49

however, unlike the block face techniques, the ATLUM technique can take advantage ofdirected high‐resolution imaging to dramatically shorten this imaging time.
As stated above, at current sectioning speeds, the ATLUM could reduce a 10mm3 block ofbrain tissue to a tape of ultrathin sections in just a few months. Such a tape would constitute a
permanent “ultrathin section library” of the entire volume, and any part of it could be
randomly imaged (and re‐imaged) at whatever resolution desired. A researcher could
coarsely image the entire volume in a few days, find a target neuron within the volume, andthen subsequently direct high‐resolution imaging exactly where it is needed to trace thatneuron’s processes and its connections with additional neurons. In this way, a multi‐neuron
circuit, stretching throughout the entire 10mm3 volume, could be mapped with synaptic
precision while only imaging perhaps one one‐thousandth of the whole volume. In thisfashion, the ATLUM can potentially reduce the time needed to trace a complete neural circuitfrom hundreds of years to just a few months, making the V1 trial emulation experimentpotentially feasible in the near‐term.
Parenthetically, we can also note that the near‐term goal of tracing and emulating circuits of afew hundred neurons drastically reduces the image processing and computer simulation
requirements. Instead of requiring millions of neurons with billions of synapses to be traced
and identified, only the limited subset under study need be traced in the image data. The
successful completion of “partial brain” emulation studies (such as the hypothetical V1 study
above) is probably a necessary precursor to spur the type of research and investment insectioning, imaging, tracing, and emulations technologies needed for eventually scaling‐up towhole brain emulation levels.
Possibilities for increasing SEM imaging speed
From the above discussion it is clear that long imaging times constitute a major barrier towhole brain emulation using SEM techniques. However, there is currently a major research
push toward massively parallel multi‐beam SEMs which has the potential to speed up SEM
imaging by many orders‐of‐magnitude. This research push is being driven by thesemiconductor industry as part of its effort to reduce feature sizes on computer chips below
the level that traditional photolithography can produce.
The circuitry patterns within computer chips are produced through a series of etching anddoping steps. Each of these steps must affect only selected parts of the chip, so areas to be left
unaffected are temporally covered by a thin layer of polymer which is patterned in exquisite
detail to match the sub‐micron features of the desired circuitry. For current mass productionof chips this polymer layer is patterned by shining ultraviolet light through a mask onto thesurface of the silicon wafer which has been covered with the photopolymer in liquid form.
This selectively cures only the desired parts of the photopolymer. To obtain smaller features
than UV light can allow, electron beams (just as in a SEM) must instead be used to selectively
cure the photopolymer. This process is called e‐beam lithography. Because the electron beam
must be rastered across the wafer surface (instead of flood illuminating it as in light
lithography) the process is currently much too slow for production level runs.
Several research groups and companies are currently addressing this speed problem bydeveloping multi‐beam e‐beam lithography systems (Kruit, 1998; van Bruggen, van Someren
et al., 2005; van Someren, van Bruggen et al., 2006; Arradiance Inc). In these systems,
hundreds to thousands of electron beams raster across a wafer’s surface simultaneously
writing the circuitry patterns. These multi‐beam systems are essentially SEMs, and it should
be a straightforward task to modify them to allow massively parallel scanning as well50

(Pickard, Groves et al., 2003). For backscatter imaging (as in the SBFSEM, FIBSEM, andATLUM technologies) this might involve mounting a scintillator with a grid of holes (one foreach e‐beam) very close to the surface of the tissue being imaged. In this way the interactionsof each e‐beam with the tissue can be read off independently and simultaneously.
It is difficult to predict how fast these SEMs may eventually get. A 1,000 beam SEM where
each individual beam maintains the current 1 MHz acquisition rate for stained sections
appears reachable within the next ten years. We can very tentatively apply this projected SEM
speedup to ask how long imaging a human brain would take. First, assume a brain weresliced into 50nm sections on ATLUM‐like devices (an enormous feat which would itself takeapproximately 1,000 machines – each operating at 10x the current sectioning rate – a total of
3.5 years to accomplish). This massive ultrathin section library would contain the equivalentof 1.1∙1021 voxels (at 5×5×50 nm per voxel). Assuming judicious use of directed imaging
within this ultrathin section library only 1/10 may have to be imaged at this extremely highresolution (using much lower, and thus faster, imaging on white mater tracts, cell bodyinteriors etc.). This leaves roughly 1.1∙1020 voxels to be imaged at high resolution. If 1,000
SEMs each utilizing 1,000 beamlets were to tackle this imaging job in parallel their combined
data acquisition rate would be 1∙1012 voxels per second. At this rate the entire imaging task
could be completed in less than 4 years.

Nanodisassembly
The most complete approach would be to pick the brain apart atom by atom or molecule bymolecule, recording their position and type for further analysis. The scenario in (Morevec,
1988) can also be described as nanodisassembly (in an unfixated brain, with on‐the‐fly
emulation) working on a slightly larger size scale. (Merkle, 1994) describes a relatively
detailed proposal where the brain is divided into 3.2∙1015 0.4 μm cubes where each cube
would be disassembled atomically (and atom/molecule positions recorded) by a disassembler
nanodevice (Drexler, 1986) over a three year period.
It has been pointed out that medical nanorobotics is the form of nanotechnology best used fornon‐destructive scanning6. Given that no detailed proposal for a nanodisassembler has been
made it is hard to evaluate the chances of nanodisassembly. It would have to act at a lowtemperature to prevent molecules in the sample from moving around, removing surface
molecules one by one, identifying them and transmitting the position, orientation and type tosecond‐line data aggregators. Clear challenges are the construction of tool tips that can extract
arbitrary molecules or detect molecular type for further handling with specialized tool tips, aswell as handling macromolecules and fragile molecular structures. Macromolecules can likely
not be pulled out in one piece. Atomic disassembly would avoid the complications ofmolecules for the greater simplicity of a handful of atom types, at the price of needing to
break molecular bonds, the risk of ensuing rearrangements and the possibility of creating
reactive free radicals. Mature productive nanosystems would appear to be a necessary
precursor technology for this approach (probably for the production of the massively parallel
disassembly system required for controlled atomic disassembly of a macroscopic object)(Foresight Nanotech Institute and Batelle Memorial Institute, 2007). It appears likely that thekind of technology needed for disassembly would be significantly more complex than the one
needed for atomically precise assembly.

6

Robert Freitas Jr., personal communication.

51

Chemical analysis
A key challenge is to detect the chemical state and type of cellular components. Normally thisis done by staining with dyes or quantum dots that bind to the right target, followed byreadout using optical methods. Beside the need for diffusing dyes through the samples, eachdye is only selective for a certain target or group of targets, necessitating multiple dyes for
identifying all relevant components. If the number of chemicals that have to be identified islarge, this would make dyeing ineffective.
One possible approach is Raman microspectroscopy (Krafft, 2004; Krafft, Knetschke et al.,2003), where near‐infrared scattering is used to image the vibration spectrum of the chemicalcomponents (mainly macromolecules) of tissue. The resolution for near infrared spectroscopy
is about 1 μm (limited by diffraction) and confocal methods can be used for 3D imaging.
Recording times are very long, on the order of minutes for individual pixels; in order to be
useful for WBE this has to be speeded up significantly or parallelized. Using shorter
wavelengths appears to induce tissue damage (Puppels, Olminkhof et al., 1991), which may
be of little concern for destructive scanning. Ultraviolet resonance microspectroscopy has also
been used, enabling selective probing of certain macromolecules (Pajcini, Munro et al., 1997;
Hanlon, Manoharan et al., 2000). In some cases native fluorescence can enable imaging bytriggering it with UV light, laser‐induced native fluorescence, LINF, such as in the case ofserotonin (Tan, Parpura et al., 1995; Parpura, Tong et al., 1998) and possibly dopamine(Mabuchi, Shimada et al., 2001).
A new method with great promise is array tomography, a hybrid between optical
fluorescence imaging and electron microscopy (Micheva and Smith, 2007). Samples are cut
into ultrathin (50‐200 nm thick) sections forming an ordered array on a glass slide. The arrayis then labelled with fluorescent antibodies or other stains and imaged, generating a 3D
reconstruction with a depth resolution set by the sectioning thickness. After this imaging thearray can be eluted to remove the stain, restained with a new stain, imaged and so on. Finallyit can be stained with metal and imaged in a scanning electron microscope. The different
image stacks can then be combined, giving a high resolution 3D reconstruction with chemical
information.
At present it looks uncertain how much functionally relevant information can be determinedfrom spectra. If the number of neuron types is relatively low and chemically distinct, it might
be enough to recognize their individual profiles. Adding dyes tailored to disambiguateotherwise indistinguishable cases may also help.

Embedding, fixation and staining techniques
Most forms of destructive scanning require fixation and embedding the brain to be scanned
so that it can be handled, sectioned, and imaged well. This process must preserve
ultrastructure and necessary chemical information.
Current electron microscopy often relies on osmium tetroxide and other heavy metal stains tostain cell membranes for improved contrast (Palay, McGee‐Russell et al., 1962). It appears
likely that unless pure morphology can be used to deduce functional properties, improved
staining methods are needed to create a link between what can be imaged and what isfunctionally relevant. This might be an area where nanoparticles (and later, more advanced
nanodevices) will be important.

52

Conclusion
It is likely that the scanning methods used to provide raw data for WBE will be, at least in theearly days, destructive methods making use of sectioning and scanning.
Assuming that imaging the finest axonal processes and synaptic spines are required (≈ 50nm),
this sets a resolution requirement on the order of 5 nm at least in two directions. Comparing
the methods mentioned and their resolution we get the following categorization:
Table 5: Scanning methods
MethodMRI

Resolution> 5.5 μm (non‐frozen)

MRI microscopy
NIR microspectroscopy
All‐optical histology
KESM
X‐ray microtomography
MRFM
SI
X‐ray microscopy

3 μm
1 μm
0.7 μm
0.3 μm x 0.5 μm
0.47 μm
80 nm
50 nm
30 nm
Required resolution?
50‐70 nm x 1‐20 nm
30‐50 nm x 1‐20 nm
40 nm x 5 nm
50 nm x 1 nm
10 nm
5 nm?

SBFSEM
FIBSEM
ATLUM
SSET
Atomic beam microscopy
NSOM
SEM
Array tomography
TEM

1‐20 nm
1‐20 nm SEM, 50x200x200 nm
fluorescence stains<1 nm

Does not require sectioning, may
achieve better resolution on vitrified
brains.

Spectromicroscopy possible?

Not implemented yet.
Requires fluorescent markers,
spectroscopy possible.
Enables multiple staining
Basic 2D method, must be combinedwith sectioning or tomography for
3D imaging. Damage from highenergy electrons at high resolutions.

A 5×5×50 nm resolution brain scan requires 1.4∙1021 voxels, a large amount of raw data (seenext section). Destructive scanning working on fixated brains may avoid having to store theentire dataset by only storing the most recent slices of brain and perform image processing onthese. Instead of first scanning the entire brain, and then processing the image data to extract
the relevant features and neuronal network, the extraction can proceed piecemeal andconcurrently as new imaging data is collected. Once relevant information has been extracted
from a data batch, the raw visual data for that batch can be discarded.
However, ideas such as the single brain physical library (Hayworth, 2002) demonstrate thatthe scanning does not have to be single‐shot: a brain is sectioned and fixed in a suitablemanner for interactive scanning (possibly using several modalities) and retrieval. This also
gets around the data storage problem by only needing to scan regions of interest and store
their data (possibly temporarily, since they can be re‐scanned if needed later). In earlyneuroinformatics applications, this might include mapping out the long‐range connectivity ofa subset of representative neurons in order to find their morphology and connectivity, only
requiring imaging a very small subset of the brain’s volume (0.01% for mapping out 100,000
neurons in a human brain). It may also be possible to combine this approach with methods
like array tomography (Micheva and Smith, 2007) for combined chemical and structural
maps.

53

Low resolution methods such as optical microscopy are an important test of many aspects of
the WBE research endeavour, such as large‐scale data management, inferring neural function
from morphology (possibly using spectroscopy or other methods of estimating chemical
states), and providing early test data for tracing and connectivity reconstruction algorithms.
If the required level of simulation is at level 5 or above (detailed cellular electrophysiology
and neuron connectivity: see Table 2) we appear to have already achieved the resolution
requirements and the remaining problem is in data/tissue management and scaling up
methods to handle large brains. Using KESM or ATLUM it should be possible in the very
near future to construct a detailed connectome of the brain, in particular the exact
interconnections of cortical minicolumns.
If the required level for WBE is below level 5, increases of imaging resolution are of relatively
limited use. Rather, we need modalities that enable mapping the proteins, possibly theirstates and the presence of metabolites or RNA transcripts. This would pose a major researchchallenge, although it is in line with much research interest today examining the biophysics
of cells.

54

Image processing and scan interpretation
The data from the scanning must be postprocessed and interpreted in order to become usefulfor brain emulation (or other research). Cell membranes must be traced, synapses identified,neuron volumes segmented, distribution of synapses, organelles, cell types and otheranatomical details (blood vessels, glia) identified. Currently this is largely done manually:cellular membranes can be identified and hand‐traced at a rate of 1‐2 hours/μm3 (Fiala andHarris, 2001), far too slow for even small cortical volumes.
Software needed includes (after (Fiala, 2002)):• Geometric adjustment (aligning sections, handling shrinkage, distortions)• Noise removal• Data interpolation (replacing lost or corrupted scan data)• Cell membrane tracing (segmentation, tracing in 2D and 3D)• Synapse identification• Identification of cell types• Estimation of parameters for emulation• Connectivity identification• Databasing
Data handling is at present a bottleneck. 0.1 mm3 at 400 pixels/μm resolution and 50 nm
section thickness would (compressed) contain 73 terabytes of raw data. A full brain at thisresolution would require 109 terabytes (Fiala, 2002). While extremely large, even this might
one day be regarded as feasible (see Appendix B). However, as mentioned in the previous
chapter, for emulation purposes it may be far more practical to perform a sequence of
scanning and interpretation steps so that only a smaller buffer of high resolution data isnecessary.
If the brain is divided into small blocks, each block could be analyzed independently, at leastin terms of low‐level image processing and preliminary tracing. Data from neighbouring
blocks would need to be compared but there is no need for the analysis of more remote
blocks (with the possible exception of interpolating lost data). This suggests that it can beparallelized to a great degree. As the scan progresses, low‐level data can be discarded tomake room for the compressed high‐level representation needed to build the emulation7.
This section deals with the assumption that WBE is achieved using image‐based methods
rather than correlation analysis methods where e.g. nanomachines record local neural activity
and estimate connectivity from the correlations in the activity pattern. Correlation methods
would have corresponding demands for signal processing and inference, but in the time
domain rather than the space domain.

Geometric adjustment
Various methods for achieving automatic registration (correcting differences in alignment) ofimage stacks are being developed. At its simplest, registration involves finding a combinationof translation, scaling, and rotation that makes subsequent images match best. However,skewing and non‐linear distortions can occur, requiring more complex methods. Combining
this with optimization methods and an elastic model to correct for shape distortion produced
7 This loss of data may be of concern since initial scans are likely to be expensive (and hence hard to repeat), and forhuman brains (which are individually valuable). Some forms of scanning such as ATLUM may produce storable‘libraries’ of slices that can be retained for re‐scanning or further analysis (Hayworth, 2002).

55

good results with macroscopic stacks of rat and human brains (Schmitt, Modersitzki et al.,2007).

Noise removal
Removing noise from images is part of standard image processing, with an extensive
literature and strong research interest. In general, noise removal is simplified when the kindsof noise introduced by scanning artefacts and the nature of the system are known. As an
example, see (Mayerich, McCormick et al., 2007) where light variations and knife chatter
noise were removed from KESM data.

Data interpolation
Lost/corrupted data must be replaced with probabilistic interpolations. This might require
feedback from later stages to find the most likely interpretation or guess, constrained by whatmakes sense given known data.
For large lost volumes, generic neurons and connectivity might have to be generated basedon models of morphology and connectivity. The goal would be to avoid changing thefunctionality of the total network. Early results in WBE and statistics from successful imaging
volumes should provide much useful input in developing this.
Sectioning the brain into individually scannable chunks will introduce data loss and possible
misalignment along the edges (for example, the KESM suffers damage up to 5 μm in width
between different columns (Kwon, Mayerich et al., 2008)). This may prove to be the major
source of lost data in WBE. Since this could cause mis‐tracing of long axons crossing
numerous chunk boundaries, solving this issue is a high priority. Alignment can probably beachieved reliably if the lost zone is sufficiently smaller than the correlation length in thesurrounding images.

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Cell tracing

Figure 17: Blood vessel reconstruction from KESM Nissl data. (Copyright brain Networks
Laboratory, Texas A&M University)
Automated tracing of neurons imaged using confocal microscopy has been attempted using avariety of methods. Even if the scanning method used will be a different approach it seems
likely that knowledge gained from these reconstruction methods will be useful.
One approach is to enhance edges and find the optimal joining of edge pixels/voxels to detect
contours of objects. Another is skeletonization. For example, (Urban, O’Malley et al., 2006)
thresholded neuron images (after image processing to remove noise and artefacts), extracting
the medial axis tree. (Dima, Scholz et al., 2002) employed a 3D wavelet transform to perform
a multiscale validation of dendrite boundaries, in turn producing an estimate of a skeleton.
A third approach is exploratory algorithms, where the algorithm starts at a point and uses
image coherency to trace the cell from there. This avoids having to process all voxels, butrisks losing parts of the neuron if the images are degraded or unclear. (Al‐Kofahi, Lasek et al.,2002) use directional kernels acting on the intensity data to follow cylindrical objects.
(Mayerich and Keyser, 2008) use a similar method for KESM data, accelerating the kernel
calculation by using graphics hardware. (Uehara, Colbert et al., 2004) calculates theprobability of each voxel belonging to a cylindrical structure, and then propagates dendrite
paths through it.
One weakness of these methods is that they assume cylindrical shapes of dendrites and thelack of adjoining structures (such as dendritic spines). By using support‐vector machines thatare trained on real data a more robust reconstruction can be achieved (Santamaría‐Pang,
Bildea et al., 2006).
Overall, tracing of branching tubular structures is a major interest in medical computing. A

57

survey of vessel extraction techniques listed 14 major approaches, with several examples ofeach (Kirbas and Quek, 2004). The success of different methods is modality‐dependent.

Figure 18: 3D visualization of Golgi‐stained cell reconstructed from KESM data.
(Copyright Brain Networks Laboratory, Texas A&M University

Figure 19: 3D reconstruction of a cube (2 μm side) of neuropil from rat hippocampus.
Axons are green, dendrites ochre, astrocytes pale blue, myelin dark blue. (Copyright JSpacek, Synapse Web)58

Synapse identification

Figure 20: Reconstructed spiny dendrite of CA1 pyramidal cell, rendered from data in(Harris and Stevens, 1989). (Copyright Synapse Web)In electron micrographs, synapses are currently recognized using the criteria that within astructure there are synaptic vesicles adjacent to a presynaptic density, a synaptic density withelectron‐dense material in the cleft and densities on the cytoplasmic faces in the pre‐ andpostsynaptic membranes (Colonnier, 1981; Peters and Palay, 1996).
One of the major unresolved issues for WBE is whether it is possible to identify the functional
characteristics of synapses, in particular synaptic strength and neurotransmitter content, fromtheir morphology.
In general, cortical synapses tend to be either asymmetrical “type I” synapses (75‐95%) orsymmetrical “type II” synapses (5‐25%), based on having a prominent or thin postsynaptic
density. Type II synapses appear to be inhibitory, while type I synapses are mainly excitatory(but there are exceptions) (Peters and Palay, 1996). This allows at least some inference offunction from morphology.
The shape and type of vesicles may also provide clues about function. Small, clear vesicles
appear to mainly contain small‐molecule neurotransmitters; large vesicles (60 nm diameter)with dense cores appear to contain noradrenaline, dopamine or 5‐HT; and large vesicles (upto 100 nm) with 50‐70 nm dense cores contain neuropeptides (Hokfelt, Broberger et al., 2000;
Salio, Lossi et al., 2006). Unfortunately there does not appear to be any further distinctivenessof vesicle morphology to signal neurotransmitter type.

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Identification of cell types
Distinguishing neurons from glia and identifying their functional type requires otheradvances in image recognition.
The definition of neuron types is debated, as well as the number of types. There might be as
many as 10,000 types, generated through an interplay of genetic, posttranscriptional,
epigenetic, and environmental interactions (Muotri and Gage, 2006). There are some 30+
named neuron types, mostly categorized based on chemistry and morphology (e.g. shape, thepresence of synaptic spines, whether they target somata or distal dendrites). Distinguishingmorphologically different groups appear feasible using geometrical analysis (Jelinek andFernandez, 1998).
In terms of electrophysiology, excitatory neurons are typically classified into regular‐spiking,intrinsic bursting, and chattering, while interneurons are classified into fast‐spiking, burst
spiking, late‐spiking and regular spiking. However, alternate classifications exist. (Gupta,
Wang et al., 2000) examined neocortical inhibitory neurons and found three different kinds ofGABAergic synapses, three main electrophysiological classes divided into eight subclasses,
and five anatomical classes, producing 15+ observed combinations. Examining the subgroupof somatostatin‐expressing inhibitory neurons produced three distinct groups in terms oflayer location and electrophysiology (Ma, Hu et al., 2006) with apparently different functions.
In prefrontal cortex layer 2/3 inhibitory neurons’ morphology and electrophysiology also
produced clusters of distinct types (Krimer, Zaitsev et al., 2005).
Overall, it appears that there exist distinct classes of neurons in terms of neurotransmitter,neuropeptide expression, protein expression (e.g. calcium binding proteins), and overall
electrophysiological behaviour. Morphology often shows clustering, but there may exist
intermediate forms. Similarly, details of electrophysiology may show overlap between
classes, but have different population means.
Some functional neuron types are readily distinguished from morphology (such as the five
types of the cerebellar cortex). A key problem is that while differing morphologies likely
implies differing functional properties, the reverse may not be true. Some classes of neurons
appear to show a strong link between electrophysiology and morphology (Krimer, Zaitsev etal., 2005) that would enable inference of at least functional type just from geometry. In thecase of layer 5 pyramidal cells, some studies have found a link between morphology andfiring pattern (Kasper, Larkman et al., 1994; Mason and Larkman, 1990), while others havenot (Chang and Luebke, 2007). It is quite possible that different classes are differently
identifiable, and that the morphology‐function link could vary between species.
In many species there exist identifiable neurons, neurons that can be distinguished from otherneurons in the same animal and identified across individuals, and sets of equivalent cells thatare mutually indistinguishable (but may have different receptive fields) (Bullock, 2000).
While relatively common in small and simple animals, identifiable neurons appear to be aminority in larger brains. Early animal brain emulations may make use of the equivalence byusing data from several individuals, but as the brains become larger it is likely that allneurons have to be treated as individual and unique.

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Estimation of parameters for emulation(Markram, 2006) lists the following necessary building blocks for reconstructing neural
microcircuits:
Table 6: Necessary data for reconstructing neural microcircuits
Network

Total number of neurons
Gene expression profiles

Neurons

Electrophysiological
profilesMorpho‐electrophysiological
types

Morphological profiles

Presynaptic
Structuralconnectivity
Postsynaptic

Synaptic biophysics
Functional
connectivity
Synaptic dynamics

Ion channel
compositions
Ion channel
distributions
Frequencies ofoccurrence

Total number of boutons
Total number of targets
Terminals per connection
Presynaptic innervation pattern
Total synapse numbers
Synapses per connection
Postsynaptic innervation patterns
Transmission statistics
Connection conductances
Synapse conductances
Neurotransmitter receptors
Ionic permeabilities
Reversal potentials
Probabilities of release
Depression time constants
Facilitation time constants
Long‐term plasticities

Many of these are in turn multivariate, such as ionic permeabilities or the list of
conductances.
Neuron data in the Neocortical Microcircuit Database (Markram, 2005) include 135
electrophysiological parameters derived from responses to stimuli, 234 geometric parameters
based on neural reconstruction and 51 genetic properties from single cell RT‐PCR data. For
synapses data on involved cell identities, anatomical properties (15 parameters),
physiological properties (reversal potential and pharmacological block), synaptic dynamics (5parameters) and kinetics (14 parameters).

Electrophysiology
Given electrophysiological data of a cell’s responses to simple current stimuli it is possible toreplicate the response in a compartment model (in particular the density of ionic channels)
through automated methods such as genetic algorithms and simulated annealing(Druckmann, Banitt et al., 2007; Keren, Peled et al., 2005; Vanier and Bower, 1999; Van Geit,
Achard et al., 2007). However, fitting experiments have shown that the same neural activity
can be produced by different sets of conductances (Achard and De Schutter, 2006). This may
suggest less need to detect all ionic conductances if done by other means, but the results alsosuggest that the set of good models form relatively isolated hyperplanes: relatively precise
fitting of all parameters is needed to get good performance.

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Gene expression
Different inhibitory interneurons show different gene expression for different receptors, ion
channels and gap junction‐forming proteins (Blatow, Caputi et al., 2005). Strong correlations
between gene expression of certain ion channels and conductances have been observed
suggesting that function can be inferred from genetic data (Toledo‐Rodriguez, Blumenfeld etal., 2004). Different neurons show variable expression levels, yet different neuron types haveunique patterns (Schulz, Goaillard et al., 2006; Schulz, Goaillard et al., 2007). Differences inalternate splicing have been found in individual cells within a neuron population, but there
are also differences in splicing in different brain regions suggesting there are overall
regulatory mechanisms (Wang and Grabowski, 1996).
If scanning can detect relevant mRNA levels (and the link between these and functional
properties have been found using high‐throughput analysis of all kinds of neurons) it would
likely be possible to deduce functional type.

Detecting synaptic efficacies
Normally synaptic properties are determined electrophysiologically by triggering action
potentials in the presynaptic neuron and measuring the response in the postsynaptic neuron.
This can detect not just the general strength of the synapse but also adaptation properties.
There are cases where electrophysiological properties appear to be linked to detectabledifferences in synaptic morphology, in particular the appearance of vesicle ribbons (Fields
and Ellisman, 1985; Hull, Studholme et al., 2006). LTP likely affects synaptic curvature, sizeand perforations of the postsynaptic density in a time dependent manner (Marrone and Petit,
2002; Marrone, 2007). It is commonly assumed that this, as well as more short‐term
electrophysiological changes, involves remodelling of the cytoskeleton in response toplasticity (Dillon and Goda, 2005; Chen, Rex et al., 2007). Particular proteins, such as profilin,a regulator of actin polymerisation, are activated by LTP‐inducing stimuli and may indicate
synapses that are potentiating (Ackermann and Matus, 2003).
Many synapses are “silent”, lacking membrane AMPA‐receptors; stimulation can produce atransition to an active “vocal” state (Kullmann, 2003). Silent synapses do not appear different
in micrographs, suggesting a negative answer to the question of whether function can beinferred from EM structure (Atwood and Wojtowicz, 1999). This means that for WBE
detecting at least AMPA may be necessary; this could likely be achieved using array
tomography.

Connectivity identification
This step assigns synaptic connections between neurons.
At present, statistical connectivity rules are used based on proximity, the “Peters’ rule”, inwhich synaptic connections are assumed where axons with boutons overlap with dendrites(Braitenberg and Schuz, 1998; Peters, 1979). This can be used to estimate the statistics ofsynaptic connectivity (Binzegger, Douglas et al., 2004; Shepherd, Stepanyants et al., 2005;
Kalisman, Silberberg et al., 2003). However, neural geometry cannot predict the strength offunctional connections reliably, perhaps because synaptic plasticity changes the strength ofgeometrically given synapses (Shepherd, Stepanyants et al., 2005).

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It is not possible to rely on synapses only occurring from axons to dendrites; axo‐somatic,
axo‐axonic, and dendro‐dendritic (which may be one‐way or reciprocal) synapses have been
observed. Occasionally, several synapses coincide, such as serial axoaxodendritic synapses
and synaptic glomeruli where an axon synapses onto two dendrites, one of which also
synapses on the other.
One possibility is that there exists enough stereotypy (repeating patterns of structural andfunctional features) in the brain to simplify connectivity identification (and possiblyinterpolation) (Silberberg, Gupta et al., 2002). Such information would constrain theinterpretation process by ruling out certain possibilities, which would at the very least enablea speedup. Acquiring the potentially very complex information about stereotyped
arrangements would be one of the earliest applications and benefits of detailed scanning andmassive neuroinformatics databases. However, deviations from stereotypy may be ofparticular importance in achieving person WBE since they can represent individual variations
or characteristics.
Gap junctions, where the pre‐ and postsynaptic cells are electrically linked by connexon
channels through the cell membrane, can be identified by membranes remaining parallel withjust 2 nm separation and a grid of connexons. They appear to be relatively rare in mammals,
but occur at least in the retina, inferior olive and lateral vestibular nucleus (Peters and Palay,
1996).
At least in simple nervous systems such as C. elegans gene expression contains significant
information about its connectivity signature (Kaufman, Dror et al., 2006). At the very least,
the availability of this kind of information could be used to constrain neuron types andconnectivity.

Conclusion
Image processing is a mature area with many commercial and scientific applications.
Development of basic processing for handling the scan data does not appear to pose many
problems beyond the need for extremely high‐throughput signal and image processing, andperhaps the data management issues of the raw scans.
The neuroinformatics/WBE‐related further processing steps pose a research challenge.
Identifying cellular objects, in particular connectivity and synapses, is a non‐trivial image
interpretation problem that is currently being studied. Basic contouring algorithms appear towork reasonably well, and it is likely, given other results in current image recognition, thatsynapse detectors could be constructed. Image interpretation is traditionally acomputationally costly operation, and may conceivably be a bottleneck in developing early
WBE models (in the long run, assuming improvements in computer hardware necessary
anyway for large‐scale emulation, this bottleneck is unlikely to remain).
The hardest and currently least understood issue is estimating emulation parameters fromimagery (or developing scanning methods that can gain these parameters). This represents
one of the key research issues WBE need to answer (if only tentatively) in order to assess its
viability as a research program.

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Neural simulation
The area of neural simulation began with the classic Hodgkin and Huxley model of the action
potential (Hodgkin and Huxley, 1952). At the time, calculating a single action potential using
a manually cranked calculator took 8 hours of hard manual labour. Since then the ability tocompute neural activity across large networks has grown enormously (Moore and Hines,
1994).

How much neuron detail is needed?
It is known that the morphology of neurons affects their spiking behaviour (Ascoli, 1999),
which suggests that neurons cannot simply be simulated as featureless cell bodies. In somecases simplifications of morphology can be done based on electrical properties (Rall, 1962),
but it is unlikely this is generic.
An issue that has been debated extensively is the nature of neural coding and especiallywhether neurons mainly make use of a rate code (where firing frequency contains the signal)or the exact timing of spikes matter (Rieke, Warland et al., 1996). While rate codestransmitting information have been observed, there also exist fast cognitive processes (such as
visual recognition) that occur on timescales shorter than the necessary temporal averaging forrate codes. Neural recordings have demonstrated both precise temporal correlations between
neurons (Lestienne, 1996) and stimulus‐dependent synchronization (Gray, Konig et al., 1989).
At present, the evidence that spike timing is essential is incomplete, but there does not appear
to be any shortage of known neurophysiological phenomena that could be sensitive to it. In
particular, spike timing dependent plasticity (STDP) allows synaptic connections to bestrengthened or weakened depending on the exact order of spikes with a precision <5 ms(Markram, Lubke et al., 1997; Bi and Poo, 1998). Hence it is probably conservative to assume
that brain emulation needs a time resolution smaller than 0.4–1.4 ms (Lestienne, 1996) inorder fully to capture spike timing.
The time resolution is constrained by the above spike timing requirement as well as numericconsiderations in modelling action potentials (where the dynamics become very stiff). Ion
channels open in 0.1 ms, suggesting that emulations that do not depend on individual
detailed ion channel dynamics might need this time resolution. If they also take individual
ion channels and enzymes into account, they would have to be 1‐3 orders of magnitude moredetailed.
One of the most important realizations of computational neuroscience in recent years is thatneurons in themselves hold significant computational resources. “Dendritic computing”
involves nonlinear interactions in the dendritic tree, allowing parts of neurons to act as
artificial neural networks on their own (Single and Borst, 1998; London and Hausser, 2005;
Sidiropoulou, Pissadaki et al., 2006). It appears possible that dendritic computation is asignificant function that cannot be reduced into a point cell model but requires calculation ofat least some neuron subsystems.

Internal Chemistry
Brain emulation needs to take chemistry more into account than commonly occurs in current
computational models (Thagard, 2002). Chemical processes inside neurons have64computational power on their own and occur on a vast range of timescales (from sub‐millisecond to weeks). Neuromodulators and hormones can change the causal structure ofneural networks, e.g. by shifting firing patterns between different attractor states.
About 200 chemical species have been identified as involved in synaptic plasticity, forming acomplex chemical network. However, much of the complexity may be redundant parallel
implementations of a few core functions such as induction, pattern selectivity, expression ofchange, and maintenance of change (where the redundancy improves robustness and offers
the possibility of fine‐tuning) (Ajay and Bhalla, 2006).
At the very low numbers of molecules found in synaptic spines, chemical noise becomes asignificant factor, making chemical networks that are bistable at larger volumes unstablebelow the femtoliter level and reducing pattern selection (Bhalla, 2004b, a). It is likely thatcomplex formation or activity constrained by membranes is essential for the reliability ofsynapses. However, this may not necessarily require detailed modelling of the complexes
themselves, just of their statistics.
Proteomics methods are being applied to synapses, potentially identifying all present
proteins (Li, 2007). The Synapse protein DataBase contains about 3,000 human synapse‐
related proteins (Zhang, Zhang et al., 2007), although it is likely that many of these are not
involved in the actual synaptic processing.
Of the proteins coded by the human genome, around 3.2% (988) have been predicted to be
regulatory molecules, 2.8% (868) kinases, 5.0% (1543) receptors, 1.2% (376) signalling
molecules, and 1.3% (406) ion channels. If the relative proportions are the same among the
unknown proteins (41.7%, 12,809), the numbers should be scaled up by 1.4 (VenterAdams etal., 2001). Posttranslational modifications produce on average 3‐6 different proteins per gene
(Wilkins, Sanchez et al., 1996). This places an upper limit on the number of protein types
directly involved in neural signalling and internal signal transduction on the order of 35,000.
If all genes were involved we should expect the proteome to be around 158,000 proteins.
Later estimates have run up all the way to ≈1 million proteins (and 600,000 immunoglobulins
varying in epitope binding, likely irrelevant for WBE) (Humphery‐Smith, 2004), althoughmost estimates appear to tend towards the ≈100,000 range.
If the requirements for WBE are on the kineome level, then for each involved protein anumber of species may be needed, depending on the number of possible phosphorylation,
dimerization, carboxylation, or other altered states that exist. In principle, this could lead to a
combinatorial explosion of types, requiring storing data for individual protein molecules if
the number of functionally relevant kinds is very large.
It is common to simulate cellular regulatory networks using mass action laws and Michaelis‐Menten kinetics, although this assumes free diffusion and random collisions. This assumption
does not always hold, since molecular mobility inside cells is limited by the properties of thecytoplasm, compartmentalization, and protein anchoring to surfaces. This can be at least
partially taken into account by adjusting the equations for “fractal kinetics”. Whether fractal
kinetics or mass action is valid depends mainly on the probability of reactions (Grima andSchnell, 2006; Schnell and Turner, 2004; Xu and Ding, 2007).
It might also be necessary to model gene expression and other epigenetic mechanisms. Long‐
term plasticity in neurons requires changes in gene expression, both as response to plasticity‐
inducing input and in regulating overall plasticity. In addition, gene expression is known to65

drive the circadian rhythm, which affects many aspects of brain function (Levenson andSweatt, 2005; Miller and Sweatt, 2007; McClung and Nestler, 2008). Whether all forms ofplasticity or other relevant changes can be simulated with sufficient detail without simulating
the gene expression network is currently unknown. If not, then the brain emulation would at
the very least have to simulate the contributing gene regulation network and at most asizeable part of the total network. Gene expression timescales are on the order of minutes tohours, suggesting that the timestep of expression simulation does not have to be as short asfor neurons if it is not done on an individual protein level.

Learning rules and synaptic adaptation
A WBE without any synaptic change would likely correspond to a severely confused mind,
trapped in anterograde amnesia. While working memory may be based on attractor states ofneural activity in the prefrontal cortex and some forms of priming and habituation plausibly
are based on synaptic adaptation and calcium build‐up, long‐term memory formation
requires changes in the strength (and possibly connectivity) of synapses.
Synaptic learning has been extensively studied in computational neuroscience, starting with
Donald Hebb’s 1949 suggestion that co‐occurring neural firing initiated long‐term change.
Since then models on all levels of abstraction have been studied, ranging from the completely
abstract to detailed synaptic biochemistry.
Many network models include various synaptic learning rules. There is a wide variety ofrules used, ranging from simple Hebbian rules with no state beyond the current weight, overthe “BCM‐rule” accounting for LTP/LTD‐effects in rate‐coding neurons (Bienenstock, Cooper
et al., 1982) and STDP that counts time differences between spikes (Senn, Markram et al.,2001), to detailed signal transduction models that include 30+ substances (Kikuchi, Fujimoto
et al., 2003; Bhalla and Iyengar, 1999).
Synapses also show various forms of adaptation to repeated firing, including both facilitationand depression, which in turn can affect the network dynamics in ways that appear likely tohave behaviourally relevant consequences (Thomson, 2000). Various models have been
constructed of how synaptic ‘resources’ are depleted and replenished (Tsodyks, Pawelzik etal., 1998). Such models usually include a few extra state variables in the synapses and time
constants for them.
For WBE it is important to keep the number of state variables and local parameters low insynapses, since they dominate the storage demands for neural/compartment models of thebrain. At present, we do not have a firm estimate of how complex synapses need to be inorder to reproduce the full range of plasticity observed. It is very likely that simple weight‐
based models are too simple and that models with a handful of state variables cover mostobserved phenomena. Whether the full transduction cascade needs to be simulated is unclear.
On the plus side, this is an area that has been subject to intense modelling and research on
multiple scales for several decades and very accessible to experimental testing. Progress insynaptic modelling may be a good indicator of neuroscience understanding.

Neural models
The first neural model was the McCulloch‐Pitts neuron, essentially binary units summing
weighted inputs and firing (i.e. sending 1 rather than 0 as output) if the sum was larger than athreshold (Hayman, 1999; McCulloch and Pitts, 1943). This model and its continuous state66

successors form the basis of most artificial neural network models. They do not have any
internal state except the firing level. Their link to real biology is somewhat tenuous, although
as an abstraction they have been very fruitful.
More realistic models such as “integrate‐and‐fire” sum synaptic potentials and produce
spikes.
Single cell‐modelling can be done on roughly five levels of abstraction (Herz, Gollisch et al.,2006): as black box modules that generate probabilistic responses to stimuli according to someprobability distribution, as a series of linear or nonlinear filters of signals, as a single
compartment with ionic conductances, as a reduced compartment model (few compartments)
or as a detailed compartmental model. The more abstract models are theoretically andcomputationally tractable and have fewer degrees of freedom, while the more detailed
models are closer to biological realism and can be linked to empirical data. According to(Herz, Gollisch et al., 2006), most task‐specific computation of known direct biologicalrelevance can be achieved by reduced compartmental models, with the possible exception ofsome forms of nonlinear dendritic processing.

Figure 21: Compartment models of neurons usually begin with an electrophysiologicallycharacterized neuron (A). This can be regarded as a network of electrical cables withindividual properties (B). These are then subdivided into isopotential compartments. The
system can then be modeled as an equivalent electronic network (C). Each ion channel typecorresponds to a pair of parallel‐connected potentials and variable resistances percompartment. Compartment simulations numerically simulate the equivalent network8.
Image from (Bower and Beeman, 1998)
Conductance‐based models are the simplest biophysical representation of neurons,
representing the cell membrane as a capacitor and the different ion channels as (variable)
resistances. Neurons or parts of neurons are replaced by their equivalent circuits, which are8

See http://www.brains‐minds‐media.org/archive/222 for a tutorial in this methodology.

67then simulated using ordinary differential equations9. Beside the membrane potential they
have (at least) two gating variables for each membrane current as dynamical variables.
The core assumptions in conductance‐based models are that different ion channels are
independent of each other; the gating variables are independent of each other, depending
only on voltage (or other factors such as calcium), first order kinetics in the gating variables;
and that the region being simulated is isopotential.
More complex ion channel models with internal states in the channels have been developed,
as well as models including calcium dynamics (possibly with several forms of calcium
buffering).
In simple neuron models, the “neuronic” (firing rate update) equations can be uncoupledfrom the “mnemonic” (synaptic weight update) equations, the “adiabatic learning
hypothesis” (Caianiello, 1961). However, realistic models often include a complex interplay atsynapses between membrane potential, calcium levels, and conductances that make thisuncoupling difficult to preserve.
A common guideline in modelling is to use compartments 1/10 to 1/20 of the length constant10of the dendrite (or axon), making potential differences between compartments differ just by≈5%. Typical length constants are on the order of 2‐5 mm, giving compartments smaller than
200‐100 μm. In heavily branching neurons, the short distance between branches forces finer
resolution, on the order of 10 μm or less. It can therefore be expected that the majority ofcompartments will be due to cortical arbors rather than the long axons through the whitematter.
The number of state variables of a neuron at least scales with the number of synapses sinceeach synapse has its own dynamics. The number of synapses is also a rough estimate of thenumber of compartments needed for an accurate morphological model. Each compartment
has to store a list of neighbour compartments, dynamical variables, and local parameters.
Synapses can be treated as regular compartments with extra information about weight,
neurotransmitters, and internal chemical state. A synapse‐resolution compartment model of1011 neurons with on the order of 104 compartments would require 1015 compartments.
If states in extracellular space matter (e.g. due to volume transmission, ephaptic signals or
neurogenesis), it may be necessary to divide the simulation into a spatial grid rather thancompartments following the neurons. A volume‐based simulation where the brain is divided
into size r voxels would encompass 1.4∙10‐3/r3 voxels. Each voxel would contain information
about which cells, compartments, and other information that existed inside, as well as a list of
the dynamical variables (local electric fields, chemical concentrations) and local parametervalues. For 10 μm side voxels there would be 1.4∙1018 voxels in a human brain.

9 This makes the simplifying assumption that the neuron can be divided into isopotential compartments. A more
complex model would describe the membrane state by partial differential equations; this would be highly
cumbersome. Fortunately there are no currently known phenomena that appears to require such models.
10 The length constant λ defines how quickly an imposed voltage decays with distance in a passive cable, V(x)=V0
exp(‐x/λ), λ=√((d/4)RM/RA) where d is the diameter, RM the membrane resistance per square meter and RA the axial
resistance per meter.

68

Reduced models

Figure 22: Computational cost for different neuron models versus biological plausibility.
From (Izhikevich, 2004), figure 2.
(Izhikevich, 2004) reviews both typical neural firing patterns and a number of computationalmodels of spiking neurons. He estimates both the number of possible biological featuresdifferent models can achieve and how many floating point instructions are needed per ms ofsimulation (only assuming a soma current, not taking the effects of dendrites and synapses
into account11):
Table 7: Neuron model costs
Model
Integrate‐and‐fireIntegrate‐and‐fire with adapt.
Integrate‐and‐fire‐or‐burst
Resonate‐and‐fireQuadratic integrate‐and‐fire
Izikhevich (2003)
FitzHugh‐Nagumo
Hindmarsh‐Rose
Morris‐Lecar
WilsonHodgkin‐Huxley# of biological features
3
5
10
12
6
21
11
18
14*
15
19*

FLOPS/ms
5
10
13
10
7
13
72
120
600
180
1200

* Only the Morris‐Lecar and Hodgkin‐Huxley models are “biophysically meaningful” in thesense that they attempt actually to model real biophysics, the others only aim for a correct
phenomenology of spiking.
The (Izhikevich, 2003) model is interesting since it demonstrates that it may be possible toimprove the efficiency of calculations significantly (two orders of magnitude) without losing
too many features of the neuron activity. The model itself is a two‐variable dynamical systemwith two model parameters. It was derived from the Hodgkin‐Huxley equations using abifurcation analysis methodology keeping the geometry of phase‐space intact (Izhikevich,2007). While it is not directly biophysically meaningful, it‒‐or similar reduced models of fullbiophysics‒‐may be possible computational shortcuts in brain emulation. Whether such
reductions can be done depends on whether or not the details on internal neural biophysics
are important for network‐relevant properties such as exact spike‐timing. It may also be

11 To a first approximation each compartment would require the same number of FLOPS making a
multicompartment model about 3‐4 orders of magnitude more demanding.

69

possible to apply reduction methods on sub‐neural models, but the approach requires anunderstanding of the geometry of phase space of the system.

Simulators
There exist numerous simulation systems at present. Some of the more common are GENESIS(GEneral NEural SImulation System) (Wilson, Bhalla et al., 1989; Bower and Beeman, 1998)
and Neuron (Carnevale and Hines, 2006). For a review and comparisons, see (Brette, Rudolph
et al., 2007).
Key issues for neural simulators are numerical stability, extendability and parallelizability.
The numerical methods used to integrate conductance‐based models need to both produce
accurate approximation of solutions of the governing equations and run fast. This is mademore problematic by the stiffness of some of the equations. Most neural simulators have been
designed to be easy to extend with new functions, often producing very complex softwaresystems. For WBE, this might not be necessary once the basic neuroscience has been
elucidated. Neural simulators need to be able to run on parallel computers to reach highperformance (see section below). This is a key need for WBE.

Parallel simulation
Networks, neurons, and compartments are in general just linked to nearby entities and act
simultaneously, making brain models naturally suited for parallel simulations. The main
problem is finding the right granularity of the simulation (i.e. how many and which entities
to put on each processing node) so that communications overhead is minimized, or finding
communications methods that allow the nodes to communicate efficiently.
Simulations can be time‐driven or event‐driven. A time‐driven simulation advances one
timestep at a time while an event‐driven simulation keeps a queue of future events (such as
synaptic spikes arriving) and advances directly to the next. For some neural network models,such as integrate‐and‐fire, the dynamics between spikes can be calculated exactly, allowing
the simulation efficiently just to jump forward in time to when the next spike occurs (Mattia
and Giudice, 2000). However, for highly connected networks the times between the arrivalsof spikes become very short, and time‐driven simulations are equally efficient. On the other
hand, the timestep for time‐driven models must be short enough that the discretization of
spike timing to particular timesteps does not disrupt timing patterns, or various techniques
for keeping sub‐timestep timing information must be employed in the simulation (Morrison,
Mehring et al., 2005).
Following (Brette, Rudolph et al., 2007), the computational cost per second of biological timeis of order cU∙N/dt + cP∙F∙N∙p for time‐driven simulations and (cU+cS+cQ) ∙F∙N∙p for event driven
simulations. cU is the cost per update, cP the cost for one spike propagation (assumed to besmaller than the update cost), cQ the cost of enqueuing/dequeuing a spike (assumed to beconstant), N the number of neurons, F the rate of firing, p the number of synapses per neuron
and dt the timestep. For F=1 Hz, p=10,000 and dt=0.1 ms, the timestep dependent term of thefirst equation is likely to dominate: smaller timesteps can increase the computational coststrongly. In the second equation there is no time constant dependency, but the multiplicative
term is likely to be large and can suffer if the queue management has a nontrivial cost. The
effective time constant in an event‐driven simulation is going to be on the order of 1/Fp,
making the time‐ and event‐driven simulations about equally fast. If there are gap junctions70

or dendro‐dendritic interactions the time complexity becomes much higher (Brette, Rudolph
et al., 2007).
Well‐implemented simulations tends to scale linearly with number of processors, althoughvarious memory and communications bottlenecks may occur and optimal use of caching can
give even superlinear speedup for some problem sizes (Djurfeldt, Johansson et al., 2005;
Migliore, Cannia et al., 2006). The main problem appears to be high connectivity, since inter‐
processor communications are a major bottleneck. Keeping communications to a minimum,
for example by only sending information about when and where a spike has occurred(Johansson and Lansner, 2007) or running dendritic sub‐trees on the same processor (Hines,
Markram et al., 2008), improves performance significantly. If brain emulation requires more
information than this to flow between processing nodes performance will be lower than theseexamples.

Current large-scale simulations
Representative current large‐scale brain simulations include the following (see also Appendix
C for further examples).
A simulation of 16 Purkinje cells with 4,500 compartments receiving input from 244,000
granule cells took 2.5 hours on a 128‐processor Cray 3TE (nominally 76.8 GFLOPS) to
calculate 2 seconds of simulated activity (Howell, Dyhrfjeld‐Johnsen et al., 2000). This implies
around 0.3 MFLOPS per neuron12 and a slowdown factor of 4,500.
The Blue Brain Project aims at modelling mammalian brains at various scales and levels, inparticular modelling a complete cortical column in biological detail (Markram, 2006). The
cortical column uses about 10,000 morphologically complex neurons connected with 108
synapses on an 8,000 processor BlueGene supercomputer.
The so far (2006) largest simulation of a full Hodgkin‐Huxley neuron network was performedon the IBM Watson Research Blue Gene supercomputer using the simulator SPLIT(Hammarlund and Ekeberg, 1998; Djurfeldt, Johansson et al., 2005). It was a model of corticalminicolumns, consisting of 22 million 6‐compartment neurons with 11 billion synapses, withspatial delays corresponding to a 16 cm2 cortex surface and a simulation length of one second
real‐time. Most of the computational load was due to the synapses, each holding 3 statevariables13. The overall nominal computational capacity used was 11.5 TFLOPS, giving 0.5
MFLOPS per neuron or 1045 FLOPS per synapse. Simulating one second of neural activity
took 5,942 s14. The simulation showed linear scaling in performance with the number ofprocessors up to 4,096 but began to show some (23%) overhead for 8,192 processors(Djurfeldt, Lundqvist et al., 2006).
A simulation involving 8 million simple spiking Izhikevich neurons with 6,300 synapse/cellwith a low firing rate and STDP achieved a slowdown of just 10 on a 4,096 processor Blue
Gene supercomputer. This was achieved by both optimizing the synaptic updates and by
reducing the number of inter‐processor messages by collecting all spikes from a given nodethat were sent to another node into a single message (Frye, Ananthanarayanan et al., 2007).

12 This also includes overhead. The Purkinje cell models used significantly more computations perneuron than the granule cell models.
13 Conductance, depression and facilitation.
14 Mikael Djurfeldt, personal communication.

71

An even larger simulation with 1011 neurons and 1015 synapses was done in 2005 by Eugene
M. Izhikevich on a Beowulf cluster with 27 3 GHz processors (Izhikevich, 2005). This wasachieved by not storing the synaptic connectivity but by generating it whenever it wasneeded, making this model rather ill suited for brain emulation. One second of simulation
took 50 days, giving a slowdown factor of 4.2 million.
(Johansson and Lansner, 2007) estimate the computational demands for simulating the
mammalian neocortex of different species, based on a particular model of its function.
Assuming that minicolumns organized into hypercolumns are the computational units andthat these communicate through spike trains and using a cluster architecture, they show that
it is feasible to run a network of intermediate size between rat and cat on a current
supercomputer cluster. They claim that using this kind of high‐level network (and assuming
continued linear scaling) a macaque‐sized cortex could be run on a Blue Gene/L in real‐time.

Conclusion
Current neural simulations tend to have neuron numbers and structures fitted to theavailable computers (in particular in terms of connectivity). This is important for the linear
scaling of speed with processor number and gives a performance improvement at theexpense of generality. WBE‐scale computing will likely have a computational granularity
fitted to the structure of the brain, unless the computational power available is so large thatinefficiencies due to a loose fit are irrelevant.
Models are currently not driven by scanned data, and individual variations in neuron
properties are generated by drawing from a pre‐set random distribution. While this allows
models that generate needed parameters only when required and hence avoid storing them,this is relatively rare and most implementations do use stored parameters. Hence there does
not seem to be any difference between storage requirements for WBE models and random
models, assuming the same underlying parameters and structure.
The speedup of different networks ranges over six orders of magnitude, but most are ahundredfold to a thousandfold slower than biology, and a few are close to real‐time. This is
likely more due to research practice than any inherent limitation: model sizes are selected sothat they fit available computing power. The length of the simulation is set to produce datacomparable to some biological measurement (which, for neural networks, tend to be a few
seconds long for neurocognitively interesting cases), and the length of simulation timecorresponds to what constitutes an acceptable turnaround time for a computer centre or an
office computer. Smaller simulations can run faster until they hit bottlenecks set by inter‐
processor communications speed. It is hence likely that even early WBE will be close to real‐time, with computational constraints – rather than speed – limiting the length of thesimulation.
We clearly today have computational capabilities sufficient to simulate many invertebrate
brains (such as snails, ants, and fruit flies) using compartment models on parallel clusters ofmodest size. Small mammalian brains appear computationally within reach.
Given the differences between models, implementation, computers and other parameters it ishard to reliably compare current large‐scale models to estimate trends. There is also a lack ofhistorical data, as only recently large models have become practically possible. Hence a
conservative estimate assumes that the models will grow based solely on computer power72

and not on any algorithmic improvement. If models reliably expressing sublinear
computational demands appear, they would likely make large‐scale computer power
irrelevant as a limiting factor for WBE.
The major computational load scales with the number of synapses or (for compartment
models) compartments, since they are the most numerous entities. Hence, one likely usefulmetric would be the number of synaptic updates per second, as well as the computational
demands per synapse.

73

Body simulation
The body simulation translates between neural signals and the environment, as well as
maintains a model of body state as it affects the brain emulation.
How detailed the body simulation needs to be in order to function depends on the goal. An“adequate” simulation produces enough and the right kind of information for the emulation
to function and act, while a convincing simulation is nearly or wholly indistinguishable fromthe “feel” of the original body.
A number of relatively simple biomechanical simulations of bodies connected to simulated
nervous systems have been created to study locomotion. (Suzuki, Goto et al., 2005) simulated
the C. elegans body as a multi‐joint rigid link where the joints were controlled by
motorneurons in a simulated motor control network. Örjan Ekeberg has simulated
locomotion in lamprey (Ekeberg and Grillner, 1999), stick insects (Ekeberg, Blümel et al.,2004), and the hind legs of cat (Ekeberg and Pearson, 2005) where a rigid skeleton is movedby muscles either modeled as springs contracting linearly with neural signals, or in the caseof the cat, a model fitting observed data relating neural stimulation, length, and velocity with
contraction force (Brown, Scott et al., 1996). These models also include sensory feedback fromstretch receptors, enabling movements to adapt to environmental forces: locomotion involves
an information loop between neural activity, motor response, body dynamics, and sensory
feedback (Pearson, Ekeberg et al., 2006).
Today biomechanical model software enables fairly detailed models of muscles, the skeleton,
and the joints, enabling calculation of forces, torques, and interaction with a simulated
environment (Biomechanics Research Group Inc, 2005). Such models tend to simplify muscles
as lines and make use of pre‐recorded movements or tensions to generate the kinematics.
A detailed mechanical model of human walking has been constructed with 23 degrees of
freedom driven by 54 muscles. However, it was not controlled by a neural network but rather
used to find an energy‐optimizing gait (Anderson and Pandy, 2001). A state of‐the‐art model
involving 200 rigid bones with over 300 degrees of freedom, driven by muscular actuatorswith excitation‐contraction dynamics and some neural control, has been developed formodelling human body motion in a dynamic environment, e.g. for ergonomics testing(Ivancevic and Beagley, 2004). This model runs on a normal workstation, suggesting thatrigid body simulation is not a computationally hard problem in comparison to WBE.
Other biomechanical models are being explored for assessing musculoskeletal function inhuman (Fernandez and Pandy, 2006), and can be validated or individualized by use of MRI
data (Arnold, Salinas et al., 2000) or EMG (Lloyd and Besier, 2003). It is expected that near
future models will be based on volumetric muscle and bone models found using MRI
scanning (Blemker, Asakawa et al., 2007; Blemker and Delp, 2005), as well as construction of
topological models (Magnenat‐Thalmann and Cordier, 2000). There are also various
simulations of soft tissue (Benham, Wright et al., 2001), breathing (Zordan, Celly et al., 2004)
and soft tissue deformation for surgery simulation (Cotin, Delingette et al., 1999).
Another source of body models comes from computer graphics, where much effort has gone
into rendering realistic characters, including modelling muscles, hair and skin. The emphasis
has been on realistic appearance rather than realistic physics (Scheepers, Parent et al., 1997),
but increasingly the models are becoming biophysically realistic and overlapping with74

biophysics (Chen and Zeltzer, 1992; Yucesoy, Koopman et al., 2002). For example, 30
contact/collision coupled muscles in the upper limb with fascia and tendons were generated
from the visible human dataset and then simulated using a finite volume method; thissimulation (using one million mesh tetrahedra) ran at a rate of 240 seconds per frame on asingle CPU Xeon 3.06 GHz (on the order of a few GFLOPS) (Teran, Sifakis et al., 2005). Scaling
this up 20 times to encompass ≈600 muscles implies a computational cost on the order of ahundred TFLOPS for a complete body simulation.
Physiological models are increasingly used in medicine for education, research and patient
evaluation. Relatively simple models can accurately simulate blood oxygenation (Hardman,
Bedforth et al., 1998). For a body simulation this might be enough to provide the rightfeedback between exertion and brain state. Similarly simple nutrient and hormone models
could be used insofar a realistic response to hunger and eating were desired.

Conclusion
Simulating a realistic human body is kinematically possible today, requiring computational
power ranging between workstations and mainframes. For simpler organisms such asnematodes or insects correspondingly simpler models could (and have) been used. Since theneed for early WBE is merely adequate body simulation, the body does not appear to pose amajor bottleneck.
However, since many motor actions involve a rich interplay between mechanics and nerve
signals it should not be surprising if relatively complex models are needed for apparently
simple actions such as standing or walking. Newly started emulations may need time andeffort to learn to use their unfamiliar bodies.

75

Environment simulation
The environment simulation provides a simulated physical environment for the bodysimulation. One can again make the distinction between an adequate environment simulation
and a convincing simulation. An adequate environment produces enough input to activate
the brain emulation and allow it to interact in such a way that its state and function can beevaluated. A convincing simulation is close enough to reality that the kinds of signals andinteraction that occurs is hard (or impossible) for the emulated being to distinguish fromreality.
It seems likely that we already have the tools for making adequate environments in the formof e.g. game 3D rendering engines with physics models or virtual environments such as
Second Life. While not covering more than sight and sound, they might be enough for testingand development. For emulations of simpler brains such as C. elegans simulations withsimplified hydrodynamics (similar to (Ekeberg and Grillner, 1999)) may be enough, possibly
extended with simulated chemical gradients to guide behaviour.
Convincing environments might be necessary only if the long‐term mental well‐being ofemulated humans (or other mammals) is at stake. While it is possible that a human could
adapt to a merely adequate environment, it seems likely that it would experience such anenvironment as confining or lacking in sensory stimulation. Note that even in a convincing
environment simulation not all details have to fit physical reality perfectly (Bostrom, 2003).
Plausible simulation is more important than accurate simulation in this domain and may
actually improve the perceived realism (Barzel, Hughes et al., 1996). In addition, humans
accept surprisingly large distortions (20% length change of objects when not paying direct
attention, 3% when paying attention (Harrison, Rensink et al., 2004)), allowing a great deal ofleeway in constructing a convincing environment.
What quality of environment is needed to completely fool the senses? In the following wewill assume that the brain emulation runs in real‐time, i.e., that one second of simulation timecorresponds to one second of outside time. For slower emulations, the environment model
would be slowed comparably, and all computational demands divided by the scale factor.
At the core of the environment model would be a physics engine simulating the mechanicalinteractions between the objects in the environment and the simulated body. It would not
only update object positions depending on movement and maintain a plausible physics, itwould also provide collision and contact information needed for simulated touch. On top of
this physics simulation a series of rendering engines for different senses would produce theraw data for the senses in the body model.

VisionVisual photorealism has been sought in computer graphics for about 30 years, and thisappears to be a fairly mature area at least for static images and scenes. Much effort iscurrently going into such technology, for use in computer games and movies.
(McGuigan, 2006) proposes a “graphics Turing test” and estimates that for 30 Hz interactive
visual updates 518.4‐1036.8 TFLOPS would be enough for Monte Carlo global illumination.
This might actually be an overestimate since he assumes generation of complete pictures.
Generating only the signal needed for the retinal receptors (with higher resolution for the76

fovea than the periphery) could presumably reduce the demands. Similarly, more efficient
implementations of the illumination model (or a cheaper one) would also reduce demands
significantly.

Hearing
The full acoustic field can be simulated over the frequency range of human hearing bysolving the differential equations for air vibration (Garriga, Spa et al., 2005). While accurate,
this method has a computational cost that scales with the volume simulated, up to 16 TFLOPS
for a 2×2×2 m room. This can likely be reduced by the use of adaptive mesh methods, or ray‐or beam‐tracing of sound (Funkhouser, Tsingos et al., 2004).
Sound generation occurs not only from sound sources such as instruments, loudspeakers, andpeople but also from normal interactions between objects in the environment. By simulating
surface vibrations, realistic sounds can be generated as objects collide and vibrate. A basicmodel with N surface nodes requires 0.5292 N GFLOPS, but this can be significantly reducedby taking perceptual shortcuts (Raghuvanshi and Lin, 2006; Raghuvanshi and Lin, 2007). This
form of vibration generation can likely be used to synthesize realistic vibrations for touch.

Smell and Taste
So far no work has been done on simulated smell and taste in virtual reality, mainly due tothe lack of output devices. Some simulations of odorant diffusion have been done inunderwater environments (Baird RC, Johari H et al., 1996 ) and in the human and rat nasal
cavity (Keyhani, Scherer et al., 1997; Zhao, Dalton et al., 2006). In general, an odor simulation
would involve modelling diffusion and transport of chemicals through air flow; and therelatively low temporal and spatial resolution of human olfaction would likely allow a fairly
simple model. A far more involved issue is what odorant molecules to simulate. Humans
have 350 active olfactory receptor genes, but we can likely detect more variation due todifferent diffusion in the nasal cavity (Shepherd, 2004).
Taste appears even simpler in principle to simulate since it only comes into play when objects
are placed in the mouth and then only through a handful of receptor types. However, thetaste sensation is a complex interplay between taste, smell, and texture. It may be necessary tohave particularly fine‐grained physics models of the mouth contents in order to reproduce aplausible eating experience.

Haptics
The haptic senses of touch, proprioception, and balance are crucial for performing skilled
actions in real and virtual environments (Robles‐De‐La‐Torre, 2006).
Tactile sensation relates both to the forces affecting the skin (and hair) and to how they arechanging as objects or the body are moved. To simulate touch, stimuli collision detection isneeded to calculate forces on the skin (and possibly deformations) as well as the vibrations
when it is moved over a surface or exploring it with a hard object (Klatzky, Lederman et al.,2003). To achieve realistic haptic rendering, updates in the kilohertz range may be necessary(Lin and Otaduy, 2005). In environments with deformable objects various nonlinearities in
response and restitution have to be taken into account (Mahvash and Hayward, 2004).

77

Proprioception, the sense of how far muscles and tendons are stretched (and by inference,limb location) is important for maintaining posture and orientation. Unlike the other senses,
proprioceptive signals would be generated by the body model internally. Simulated Golgi
organs, muscle spindles, and pulmonary stretch receptors would then convert body states
into nerve impulses.
The balance signals from the inner ear appears relatively simple to simulate, since it is only
dependent on the fluid velocity and pressure in the semicircular channels (which can likely
be assumed to be laminar and homogeneous) and gravity effects on the utricle and saccule.
Compared to other senses, the computational demands are minuscule.
Thermoreception could presumably be simulated by giving each object in the virtualenvironment a temperature, activating thermoreceptors in contact with the object.
Nocireception (pain) would be simulated by activating the receptors in the presence ofexcessive forces or temperatures; the ability to experience pain from simulated inflammatory
responses may be unnecessary verisimilitude.

Conclusion
Rendering a convincing environment for all senses probably requires on the order of several
hundred TFLOPS. While significant by today’s standards, this represents a minuscule fractionof the computational resources needed for brain emulation, and is not necessary for meetingthe basic success criteria of emulation.

78

Computer requirements
WBE requires significant computer power and storage for image processing andinterpretation during the scanning process, and to hold and run the resulting emulation. Bothproblems appear to be strongly parallelizable.
A way of estimating the distance between current capabilities and those needed for human
WBE is to estimate the number of entities/state variables needed to specify the emulation, therequired time resolution, and compare this to trends in computer hardware. This will depend
on what level of detail a WBE can be achieved at; full molecular simulation would require
vastly more computational power than a model using simplified neurons. For a given level ofsimulation, we can then estimate the earliest possible date‒‐assuming that Moore’s law
continues unchanged‒‐when that kind of emulation will be possible for a given amount ofmoney.
Appendix B analyses current computing trends in detail. The main conclusion is that memory
per dollar increases one order of magnitude per 4.8 years and processing power per dollar
increases one order of magnitude per 3.7, 3.9 or 6.4 years depending on whether one bases theprediction on supercomputer price/performance, supercomputer power or commodity
computers. We will use the optimistic supercomputer estimate and the more cautious
commodity estimates to get an overall range.
It should be noted that the estimates below make merely order of magnitude estimates of thenumber of entities and complexity of their storage; they are quite debatable. However, giventhat an order of magnitude complexity increase only adds circa 5 years to the estimate, exact
numbers are not necessary. We are also ignoring body and environment simulation because,
as shown in the next sections, they likely require only a fraction of the brain emulation
computations.
Table 8: Storage demands (emulation only, human brain)
Level
1# entities
Computational
moduleBrain region
connectivity

105 regions, 107
connections

3

Analog network
population model

108 populations, 1013
connections.

4

Spiking neuralnetwork

1011 neurons, 1015
connections.

5Electrophysiology

6

Metabolome

7

Proteome

8States of protein
complexes
Distribution of1015 compartments x 10
state variables = 1016.
1016 compartments x 102
metabolites= 1018.
1016 compartments x 103
proteins and metabolites= 1019.
1016 compartments x 103
proteins x 10 states = 1020
1016 compartments x 1032

9100‐1,000?

Bytes perentity?

Memorydemands (Tb)?

Earliest year,$1 million?

3? (2‐byte
connectivity, 1
byte weight)
5 (3‐byte
connectivity, 1
byte weight, 1
byte extra statevariable)
8 (4‐byte
connectivity, 4
state variables)
1 byte per statevariable
1 byte per statevariable
1 byte per statevariable3∙10‐5Present

50

Present

8,000

2019

10,000

2019

106

2029

107

2034

108

2038

109

2043

1 byte per statevariable
1 byte per state79

complexes

proteins and metabolites
x 100 states/locations.
50x2.5x2.5 nm

Full 3D EM map(Fiala, 2002)
10

Stochastic behaviourof single molecules1025 molecules

11

Quantum

Either ≈1026 atoms, or
smaller number ofquantum‐state carrying
molecules.

variable
1 byte pervoxel,
compressed.
31 (2 bytes
molecule type,
14 bytes
position, 14
bytes velocity, 1
byte state)
Qbits

109

2043

3.1∙1014

2069

?

?

For the case of a “Manhattan project” spending $109, subtract 14.4 years from these estimates.
Table 9: Processing demands (emulation only, human brain)
Level

1
2

3

4

Computational
moduleBrain region
connectivity
Analognetwork
population
model
Spiking neuralnetwork

5

Electrophysiology

6Metabolome

7

Proteome

8States ofproteincomplexes

9

Distribution of15# entities

FLOPS
per
entityTime‐steps
per secondCPUdemand(FLOPS)

100‐1,000?

?

?

105 regions,107
connections
108
populations,1013
connections
1011 neurons,
1015
connections
1015
compartments
x 10 statevariables =
1016.

?

1016
compartments
x 102
metabolites=
1018.
1016
compartments
x 103 proteinsandmetabolites =
1019.
1016
compartments
x 103 proteinsx 10 states =
1020
1016

?

Earliest
year, $1
million(commod‐ity
computerestimate)?

Earliest
year, $1
million(Super‐computerestimate)
?

?

?

?

?

1

102

1015

2023

200815

10

103

1018

2042

2019

103
FLOPS
per mspercompart
ment.
103

104

1022

2068

2033

104

1025

2087

2044

103

104

1026

2093

2048

103

104

1027

2100

2052

103

106

1030

2119

2063

“Roadrunner” at Los Alamos National Laboratory achieved 1.7 petaflops on May 25, 2008.

80

complexes

10

11

Stochastic
behavior of
singlemolecules
Quantum

compartments
x 103 proteinsandmetabolites x
100 states per
location = 1021.
1025 molecules

Either ≈1026
atoms, orsmallernumber ofquantum‐state
carrying
molecules.

103

1015

1043

2201

2111

Qbits

1013‐1020?

?

?

For the case of a “Manhattan project” spending $109, subtract 19.1/11.1 years from theseestimates, respectively.
A rough estimate for simpler brains is that a macaque brain has 14% of the human synapses,
cat brains 3%, rat 0.26%, mouse 0.1% (Johansson and Lansner, 2007). Assuming simulation
demands scale by synapse number (likely for compartment models) this means macaqueemulations on a given level can be achieved 5.4/3.2 years earlier, cat emulations 9.7/5.6 years,
rat emulations 16/9.6 years and mouse emulations 19/11 (depending on which column above
is used). Emulations of honey bees (950,000 neurons) and aplysia (20,000) appear feasible
today at a fairly high scale (51/30, 61/36 years earlier respectively). A slower decade time ofcomputer improvement produces a longer gap between animal emulations and human
emulations.

Conclusions
It appears feasible within the foreseeable future to store the full connectivity or evenmultistate compartment models of all neurons in the brain within the working memory of alarge computing system.
Achieving the performance needed for real‐time emulation appears to be a more serious
computational problem. However, the uncertainties in this estimate are also larger since it
depends on the currently unknown number of required states, the computational complexityof updating them (which may be amenable to drastic improvements if algorithmic shortcuts
can be found), the presumed limitation of computer hardware improvements to a Moore’s
law growth rate, and the interplay between improving processors and improving
parallelism16. A rough conclusion would nevertheless be that if electrophysiological models
are enough, full human brain emulations should be possible before mid‐century. Animalmodels of simple mammals would be possible one to two decades before this.

16 This can be seen in the ongoing debates about whether consumer GPU performance should be regarded as being in
the $0.2 range per GFLOPS; if WBE can be mapped to such high‐performance cheap special purpose hardware
several orders of magnitude of improvement can be achieved.

81Validation
As computer software increased in complexity, previous methods of debugging became
insufficient, especially as software development moved from small groups to large projects inlarge organisations. This led to the development of a number of software testing
methodologies aiming at improving quality (Gelperin and Hetzel, 1988). Currently
neuroscience appears to be in an early “debugging” paradigm where data and procedures
certainly are tested in various ways, but usually just through replication or as an ad hoc
activity when something unexpected occurs. For large‐scale neuroscience testing andvalidation methods need to be incorporated in the research process to ensure that the process
works, that the data provided to other parts of the research is accurate and that the link
between reality and model is firm.
An important early/ongoing research goal would be to quantify the importance of each levelof scale of phenomena to the resultant observed higher brain functions of interest. In
particular, it is important to know to what level of detail they need to be simulated in order toachieve the same kind of emergent behaviour on the next level. In some cases it might bepossible to “prove” this by performing simulations/emulations at different levels of
resolution and comparing their results. This would be an important application of earlysmall‐scale emulations and would help pave the way for larger ones.
Ideally, a “gold standard” model at the highest possible resolution could be used to test theextent to which it is possible to deviate from this before noticeable effects occur, and to
determine which factors are irrelevant for higher‐level phenomena. Some of this information
may already exist in the literature, some needs to be discovered through new computational
neuroscience research. Exploration of model families with different levels of biological detailis already done occasionally.
A complementary approach is to develop manufactured data where the ground truth is
known (“phantom datasets”), and then apply reconstruction methods on this data to see how
well they can deduce the true network. For example, the NETMORPH system models neurite
outgrowth which creates detailed neural morphology and network connectivity (Koene,2008). This can then be used to make virtual slices. Multiple datasets can be generated to test
the overall reliability of reconstruction methods.

82

Discussion
As this review shows, WBE on the neuronal/synaptic level requires relatively modest
increases in microscopy resolution, a less trivial development of automation for scanning andimage processing, a research push at the problem of inferring functional properties ofneurons and synapses, and relatively business‐as‐usual development of computational
neuroscience models and computer hardware. This assumes that this is the appropriate levelof description of the brain, and that we find ways of accurately simulating the subsystems
that occur on this level. Conversely, pursuing this research agenda will also help detect
whether there are low‐level effects that have significant influence on higher level systems,
requiring an increase in simulation and scanning resolution.
There do not appear to exist any obstacles to attempting to emulate an invertebrate organism
today. We are still largely ignorant of the networks that make up the brains of even modestly
complex organisms. Obtaining detailed anatomical information of a small brain appears
entirely feasible and useful to neuroscience, and would be a critical first step towards WBE.
Such a project would serve as both a proof of concept and a test bed for further development.
If WBE is pursued successfully, at present it looks like the need for raw computing power forreal‐time simulation and funding for building large‐scale automated scanning/processing
facilities are the factors most likely to hold back large‐scale simulations.

83

Appendix A: Estimates of the computational
capacity/demands of the human brain
The most common approach is a straightforward multiplicative estimate: given the number ofneurons, the average number of synapses and an assumed amount of information per
synapse or number of operations per second per synapse. This multiplicative method has
been applied to microtubuli and proteins too.
However, it still might be necessary to store concentrations of several chemical species,
neurotransmitter types and other data if a biologically realistic model is needed (especiallythe identities of the pre‐ and postsynaptic neurons). Some estimates of the storage
requirements of brain emulation are included in the table below.
Other estimation methods are based on analogy or constraints. (Moravec, 1999) suggested
exploiting the known requirements of image processing by equating them with acorresponding neural structure (the retina), and then scaling up the result. (Merkle, 1989a)
used energy constraints on elementary neural operations. (Landauer, 1986) attempted an
estimation based on experimental psychological memory and signal theory.
Assumption on the order of one bit of information per synapse has some support ontheoretical grounds. Models of associative neural networks have an information storage
capacity slightly under 1 bit per synapse depending on what kind of information is encoded(Nadal, 1991; Nadal and Toulouse, 1990). Extending the dynamics of synapses for storing
sequence data does not increase this capacity (Rehn and Lansner, 2004). Geometrical andcombinatorial considerations suggest 3‐5 bits per synapse (Stepanyants, Hof et al., 2002;
Kalisman, Silberberg et al., 2005). Fitting theoretical models to Purkinje cells suggests thatthey can reach 0.25 bits/synapse (Brunel, Hakim et al., 2004).
Table 10: Estimates of computational capacity of the human brain. Units have been
converted into FLOPS and bits whenever possible. Levels refer to Table 2.
SourceAssumptions(Leitl, 1995)

Assuming 1010 neurons, 1,000
synapses per neuron, 34 bit ID perneuron and 8 bit representation of
dynamic state, synaptic weights
and delays. [Level 5]Assuming microtubuli dimer statesas bits and operating on
nanosecond switching times.
[Level 10]
Based on 100 billion neurons with1,000 connections and 200
calculations per second. [Level 4]
Argues that the number ofcomputational elements in thebrain is greater than the number ofneurons, possibly even up to the1017 individual protein molecules.
[Level 8]
Assuming 2 bits learning per
second during conscious time,
experiment based. [Level 1](Tuszynski, 2006)(Kurzweil, 1999)(Thagard, 2002)(Landauer, 1986)

Computational
demands

Memory5∙1015 bits (but notesthat the data can likely
be compressed).

1028 FLOPS

8∙1019 bits

2∙1016 FLOPS

1012 bits

1023 FLOPS

1.5∙109 bits (109 bitswith loss)

84

(Neumann, 1958)(Wang, Liu et al., 2003)(Freitas Jr., 1996)(Bostrom, 1998)(Merkle, 1989a)(Moravec, 1999; Morevec,
1988; Moravec, 1998)(Merkle, 1989a)(Dix, 2005)(Cherniak, 1990)(Fiala, 2007)(Seitz)(Malickas, 1996)

Storing all impulses over a lifetime.
Memories are stored as relations
between neurons.
1010 neurons, 1,000 synapses, firing
10 Hz [Level 4]1011 neurons, 5∙103 synapses, 100
Hz, each signal worth 5 bits. [Level
5]
Energy constraints on Ranvier
nodes.
Compares instructions needed forvisual processing primitives withretina, scales up to brain and 10
times per second. Produces 1,000
MIPS neurons. [Level 3]
Retina scale‐up. [Level 3]
10 billion neurons, 10,000 synaptic
operations per cycle, 100 Hz cycle
time. [Level 4]1010 neurons, 1,000 synapses each.
[Level 4]1014 synapses, identity coded by 48
bits plus 2x36 bits for pre‐ andpostsynaptic neuron id, 1 byte
states. 10 ms update time. [Level 4]50‐200 billion neurons, 20,000
shared synapses per neuron with
256 distinguishable levels, 40 Hz
firing. [Level 5]1011 neurons, 102‐104 synapses, 100‐
1,000 Hz activity. [level 4]1∙1011 neurons, each with 104
compartments running the basic
Hodgkin‐Huxley equations with
1200 FLOPS each (based on(Izhikevich, 2004)). Eachcompartment would have 4
dynamical variables and 10
parameters described by one byte
each.

1020 bits
108432 bits (See footnote
17)1014 bits/second
1017 operations per
second2∙1015 operations per
second (1013‐1016 ops/s)
108 MIPS

1012‐1014 operations per
second.
1016 synaptic ops/s

8∙1014 bits.

4∙1015 bits (forstructural information)
1013 bits

256,000 terabytes/s

2∙1016 bits (forstructural information)

2∙1012 synaptic
operations per secon

4∙1015 ‐ 8∙1015 bits

1015‐1018 synaptic
operations per secon
1.2∙1018 FLOPS

1.12∙1028 bits

17 This information density is far larger than the Bekenstein black hole entropy bound on the information
content in material systems (Bekenstein, 1981).

85

Appendix B: Computer Performance
Development
Forecasts of future computer performance are needed to make rough estimates of when brain
emulation will become feasible in terms of computer storage and processing capacity. The
following estimates are (with two exceptions) based on data from John C. McCallum’s
datasets of CPU price performance (McCallum, 2003), memory performance (McCallum,
2007b) and disk performance (McCallum, 2007a).

Processing Power
Plotting the performance of computer systems gives an estimate of available total computer
power of unitary, off‐the‐shelf systems (Figure 23).

Figure 23: Processing power (MIPS) over time.
A least squares fit has been made with an exponential curve (straight in this semilog plot).
The decade time, the time it takes for an order of magnitude increase in MIPS is ≈7.1 years.

86Figure 24: Processing power per dollar over time.
A more relevant measure is the amount of computing power a dollar can buy. Using the data
and adjusting for inflation to 2007 dollars (this has been done in all estimates below) we get a
decade time of just 5.6 years (Figure 24). The bootstrap 95% confidence interval is 5.3‐5.9.
Present performance is about 1 MIPS per dollar. The trend appears reliable, but possibly
accelerating.
Is the rate of development changing? Fitting curves to 20‐year intervals of the MIPS/$
estimates produces a range of estimates of the development exponent, clustering together
into two or three groups. The decade times are roughly 4.4, 8.7 and 3.5 years respectively. The
slowest development speed occurred during the 70’s and 80’s, perhaps due to theproliferation of cheap home computers built with less powerful processors for economicalreasons. Since then it has picked up speed again.
Fitting to computers in the same price classes (prices equal within an order of magnitude)
produce decade times ranging from 4.3 to 7.1 years with the cheapest computers becoming
more economical fastest. Clearly, the commodity market is driving development fast (butparallelisation has the potential to speed development even further, as described below).
Given these results it looks likely that the trend will continue (for some unknown duration)with a decade time of between 3.5 and 8.7 years, with 5.6 as a middle estimate.

FLOPS vs. MIPS
These measurements have used MIPS as a measure of computational power, but it is not areliable indicator of actual performance in numeric‐heavy applications (e.g. see (Nordhaus,87

2001) for a criticism). For that, the MFLOPS (Million FLoating Point instructions per Second)is more suitable. Unfortunately, it is not as widely tested as the MIPS.

Figure 25: Linpack performance per dollar over time.
The floating‐point heavy measure maximum LINPACK performance per dollar, produces agraph similar (if noisier) to MIPS (Figure 25). The decade time is 7.7 years, similar to the MIPS
estimate but with greater uncertainty (the bootstrap confidence interval is 6.5‐9.2 years).

88Figure 26: MIPS vs. MFLOPS.
The logarithms of estimated MIPS and max FLOPS correlate to 0.79, suggesting a fairly robust
link between them. Fitting a relationship suggests that FLOPS scales as MIPS to the power of
0.89, i.e. slightly slower than unity.
Is this exponent an artefact of early computers where numerical operations were done by one
or more instructions and not representative of current computers where co‐processors andother optimizations allow multiple numerical operations in the same clock cycle?

89

Figure 27: Fitted conversion exponent from MIPS to FLOPS for different 5 year intervals.
Fitting to smaller time intervals (length 5 years) from 1970 to 2000 produced a range of
exponents (Figure 27). To test their reliability their values were calculated for each interval
several times, each time with a different sample removed, producing a distribution (shown asgrey dots) of exponents that allowed a calculation of mean and standard deviation (shown inblue). The more reliable exponents are all close to 1, with a statistically significant increasesince 1986 until 1995. However, any long‐term trend towards higher exponents seems hard to
support.
Is the effect different in smaller computers than supercomputers? Plotting MIPS vs FLOPS,
colouring the samples by their price, and then fitting exponentials produces the following
graph (Figure 28).

90

Figure 28: Relation between MIPS and FLOPS for different price range computers.
This graph seems to suggest that the more expensive (and presumably more powerfulcomputers) produce fewer FLOPS per MIPS than the cheaper ones, which have a slightly
superlinear relation. However, this may be biased by the small sample of computers in thedata set for which information about both FLOPS, MIPS, and price is available.
Altogether, assuming that FLOPS grow as MIPS to the power of 0.8 is probably a safe
assumption, but the exponent could become >1 if there were a focus on high performance
calculation in processors (or other computer parts such as graphics cards). There is also a
sizeable spread of proportionality constants, at least two orders of magnitude.
Putting the previous considerations together, and assuming a 1 MIPS/$ 2007 we should
expect F MFLOPS of performance achievable with a price of P dollars in (T/0.8) log10(F/2.3P0.8)
years, assuming a decade time of T.

Other estimates
William D. Nordhaus has done historical estimate of computer performance since the 1800’s,
including pre‐electronic computers (Nordhaus, 2001) (partially based on data from (Morevec,
1988)). He uses millions of standardized operations per seconds (MSOPS) as his measure, 1
MSOPS roughly corresponding to 20 million 32‐bit additions18. MSOPS correlates strongly
with additions per second and cycles per second.

18 The calculation used is SOPS = 0.05 ((6 + log2 (memory) + word length)/((7 x add time + multiplicationtime)/8))

91Figure 29: Nordhaus data of computer performance per dollar over time.
The data show a sharp breakpoint around 1940 when electronic computers began to develop.
Before this time, performance per price was nearly stationary; afterwards it grew
exponentially. The decade time is 5.6 years, agreeing with previous estimates. He also
observes that supercomputer performance per price seems to lag after performance ofcommodity computers.
The data in (Kurzweil, 1999) tell a similar story, with decade time of 6.7 years.

92

Figure 30: Peak performance of Top500 dataset of supercomputers.
The Top500 supercomputing list19 tabulates the 500 most powerful supercomputers in theworld. Over 1993‐2003 these machines had a doubling time for the Linpack test of 13.5
months (Feitelson, 2005), giving a decade time of just 3.82 years. Using the full dataset 1993‐2008 gives a slightly shorter decade time, 3.7 years (this occurs both when using the general
Linpack performance or the peak performance). A likely reason for the faster growth rate
than single processor systems is that the number of processors per computer is also
increasing exponentially. While the data does not include price information, the at present(June 2008) top system achieves 1 petaflops performance for ≈$100 million, achieving 10
megaflops/$. This appears to break previous trend predictions, suggesting that extreme high‐performance systems may actually be improving much faster than the smaller systems.
However, as smaller systems begin to use parallel computation the same rapid expansion
may occur there too.

19

www.top500.org

93

Figure 31: Projected supercomputer performance from www.top500.org.

Estimating available computing power for WBE
There are three ways of estimating the computing power available for doing WBE. The first is
to extrapolate the performance to price ratio of commodity computers, the second is to use
top supercomputer performance directly, the third is to estimate the performance to priceratio for supercomputers. All three have merits and problems.
Commodity computers represent a lower bound: they are not very parallel so far and theeconomic forces driving them are not all aiming for maximum performance or evenperformance per dollar. We have data stretching back longer for them than for highperformance computers. Using the above data produces the formula[MFLOPS/$] = 2.33∙100.88(t‐2007)/D
where t is the year and D is the decade time of MIPS, 5.6 years (between 3.5 and 8.7 years
with 95% confidence).
Absolute supercomputer performance followed a very regular exponential growth between
1993 and 2006, and has enabled reliable short‐term predictions (Strohmaier and Meuer, 2004).
The growth is faster than for commodity computers due to rapidly increasing parallelisation.
However, there is reason to believe this parallelisation growth may conflict with therequirements of energy, cooling and space in the near future (see below). Since a WBE projectis likely to be a major research undertaking, having a top 500 supercomputer available is very
likely (several of the current top supercomputers have been used for computational
neuroscience); the full cost of the computer may be shared between the WBE project andother projects.

94Assuming a computer on par with the #500 computer, the available power grows as[FLOPS] = 8.996∙106 ∙10(t‐2008)/D
Where D is 3.92. Up to two orders of magnitude more computer power is available by using
more powerful computers. The cost of the computer itself would be on the order of $10
million.
Estimating supercomputer performance/price ratios is harder since their prices are notgenerally listed. Using the Roadrunner computer to calibrate (~$100 million) would give acurrent conversion factor of 1.026 petaflops / $100 million, increasing with a decade time of
3.7 years. The range of ratios at each time also likely spans at least an order of magnitude: theʺpersonal supercomputerʺ Microwulfʹs price/performance ratio (2007) was $48/Gflop20
while the Sunʹs Sparc Enterprice M9000 mainframe (base price of $511,385) produced 1.03
TFLOPS of measured performance, making its PPR > $496/Gflop. Similarly custom‐built
hardware can likely improve the ratio by at least 1‐2 orders of magnitude (Wehner, Oliker etal., 2008). Using this we get an estimate of[MFLOPS/$] = 10.26∙10(t‐2008)/D
Where D=3.7 years and the uncertainty is at least one order of magnitude at any point in time.

MemoryFigure 32: RAM memory per dollar over time.

20

http://www.calvin.edu/~adams/research/microwulf/

95

The amount of RAM memory per dollar grows neatly exponentially, with a decade time of 4.8
years.

Figure 33: RAM access speed over time.
The access times of RAM decline in the data, although numbers are only available from thelate 80’s and onwards. The curve fit produces a decade time of 18.9 years.

96Disc drives

Figure 34: Disc storage per dollar over time.
A similar trend to the exponential price‐performance growth “Moore’s law” in computers is“Kryder’s law” in disc drives (Walter, 2005). The amount of storage per dollar began to growfaster around 1980 (a curve was fitted for 1980‐2005), with a decade time of only 3.5 years(Figure 34). Relatively few disk access times are available, but fitting an overall exponential
trend gives a decade time of 29.3 years (Figure 35).
Given the growing ratio between storage capacity and speed of both RAM and drives,
updating all of the stored data will take longer and longer: storage capacity is outrunning theaccess speed. This may force finer granularity on brain emulations so that there are more
processors with their own memory rather than few very powerful processors being limitedby the amount of data they can request.

97

Figure 35: Disc access time over time.

Future
The issue is not whether there exist physical systems able to perform the computations doneby brains, since such systems already exist: the brains themselves. Rather, the issue is whether
brain‐emulating hardware can be constructed by human ingenuity in the foreseeable future ata sufficiently low cost to make WBE feasible.
While Moore’s law and other “exponential” progress laws in computing have held relatively
steady over decades, they will presumably break at some point. At the very limit, thequantized nature of the universe is likely to limit them. Before that point, limitations onmolecular building materials, light speed, and energy dissipation will prove increasingly
problematic.
Semiconductors still have a long way to go (Meindl, Chen et al., 2001). The international
semiconductor roadmap (ITRS07, 2007) appears relatively confident within the 2016 time
horizon, and lists the research challenges needed to continue the trend towards 2022.
However, this is still relatively close compared to the timescales likely for achieving WBE.
Worse, improved individual chip performance may not necessarily carry over directly into
improved supercomputer performance.
(DeBenedictis, 2004), a study of the limits of current technology trends found that the main
problem in the not too distant future (beyond 10‐20 years) is going to be heat dissipation, atleast for massively parallel computers21. The expected performance in this “end game”
21 The exception may be architectures dominated by memory rather than processing, which can remain dense andcool to a greater extent. WBE applications, however, are likely to need to update most of the state variables each98

beyond 2016 would be in the exaflops to zettaflops range (1018‐1021). While cooling can bemade more effective by dispersing the computation units this introduces communications
delays. Reversible logic may reduce heat dissipation but the best current implementations
still dissipate two orders of magnitude more energy than the theoretical minimum. The study
argues that a cost effectiveness of 5‐8 TFLOPS/$ and year (given running costs and pro‐rated
purchase price) is achievable. More efficient cooling, or less dissipative components arerecognized as important for zettaflops computing (DeBenedictis, Kogge et al., 2006).
Exactly what could supersede semiconductors remains conjectural but under intense research(Welser, Bourianoff et al., 2008; Compaño, Molenkamp et al., 1999). A few of the possibilities
that are being pursued or considered are:
•

•

•

•

•

•

•

Y‐branch switches are nanoelectronic components that use electrons moving in theballistic regime that are not stopped by barriers (Forsberg and Hieke, 2002). Suchdevices could be used to build reversible logic gates, reducing energy dissipation(Forsberg, 2004).
Rapid Single Flux Quantum (RSFQ) Logic uses superconducting quantum effects toswitch, using the flux quantum as a bit (Likharev and Semenov, 1991). It can switch
extremely fast (hundreds of gigaherz), has a low power consumption and existing
chip technology can be adapted to make RSFQ circuitry (Zinoviev, 1997). Simple
microprocessors have been demonstrated (Yoshikawa, Matsuzaki et al., 2002).
However, RSFQ requires low temperatures for superconductivity.
Optical computing. Using photons rather than electrons would increase transmission
speeds if suitable nonlinear components can be found. The field has been pursued on
and off since the 1960s with a variety of technologies (Sawchuk and Strand, 1984;
Higgins, 1995). All‐optical gates are approaching the performance of their electronic
counterparts but still need further development (Zhang, Wang et al., 2005). A key
problem is that optical communication tends to require more power over shortdistances than electronics, due to the need to avoid shot noise.
Quantum dot cellular automata (QCA). Electron configurations in patterns ofquantum dots act as a cellular automaton (Tougaw and Lent, 1994; Li, Wu et al., 2003;
Robledo, Elzerman et al., 2008; Amlani, Orlov et al., 1999). Since QCA do not transmit
currents and perform reversible computation (Timler and Lent, 2003) heat dissipation
is low, and may provide an ideal environment for zettaflops computing(DeBenedictis, 2005). While current dots are semiconductors smaller and moretemperature resistant systems could be constructed out of molecular quantum dots
(Lieberman, Chellamma et al., 2002). A related technology is “spintronics”, where
magnetic polarisation is used for computing (Allwood, Xiong et al., 2005; Imre, Csaba
et al., 2006). While also dissipating little heat, speed limitations may make it
unsuitable for rapid processing.
Helical logic, reversible logic using electrons constrained to helical pathways shiftedby an external field (Merkle and Drexler, 1996). While reversible it requires low
temperatures to function and has speed limitations.
Molecular electronics, where traditional electronics such as diodes or transistors are
reproduced on the molecular level (Tseng and Ellenbogen, 2001). Logic circuits(Bachtold, Hadley et al., 2001) and non‐volatile memory (Rueckes, Kim et al., 2000)
based on carbon nanotube transistors have been demonstrated.
Intramolecular nanoelectronics, where switching occurs on the molecule level. This
would in principle be able to reach the ultimate bit densities of 1012 bits/cm2,

timestep, which means they are unlikely to be storage‐dominated.

99

•

switching cycles of 10 ps and 1 eV energy per bit cycle (Compaño, Molenkamp et al.,1999). It would either require high‐precision self‐assembly or assembly throughmolecular nanotechnology.
Rod logic, computing using nanoscale rigid “rods” interacting mechanically, wasoriginally suggested mostly as a proof‐of‐concept that computing could be done on
the nanoscale (Drexler, 1992). Variants of rod logic can implement reversible
computation (Merkle, 1993), making it suitable for very high density processing. It
would also require molecular nanotechnology for construction.

Pessimistic claims are often made to the effect that limitations of current technology are
forever unsurpassable, or that theoretically possible technological systems such as the above
will be too costly and practically difficult to ever become feasible. Still, given that computer
technology has developed in a relatively stable manner despite several changes of basic
principles (e.g. from flip‐flops via core memory to several semiconductor generations, fromvacuum tubes to transistors to integrated circuits etc) there is no strong reason to assume they
will break because current technology will eventually be replaced by other technologies.
Large vested interests in continuing the growth are willing to spend considerable resources
on closing technology gaps. A more likely end of growth scenario is that the feedback
producing exponential growth is weakened by changes in the marketplace such as lowered
demand, lowered expectations, rising production facility costs, long development times or
perhaps more efficient software22.
If further miniaturization proves hard to achieve for one reason or another, increased
performance can still be achieved by using parallelism. If many units are sold, prices will godown even if the processors are not more powerful, and a slower exponential growth of MIPS
per dollar will continue. It should be noted that, since the commodity market has driven
development of computers very strongly, and at present only about 15% of humanity owns acomputer, there are still large untapped markets that will over time become rich enough toafford computers and hence drive further technological development. As demonstrated byvarious ingenious uses of GPU chip programming for high performance scientificapplications, it is entirely possible to profit from a completely unrelated consumer demand if
the software is adapted to high‐performance hardware even when this hardware wasdeveloped for a completely different purpose (in this case computer games). The mainconcern for WBE is that it is entirely possible for consumer computing to go in a direction lesssuitable for emulation software (e.g. trading processing power for lower power usage).

22 As an example, the exponential growth of computing power hides software bloat, where new software tends torequire increasingly more resources to do the same tasks as old versions. This is partially due to trading program
efficiency for programming simplicity. The expectation of exponentially growing hardware capabilities makes it
worthwhile to get new hardware often, which together with the bloat feeds the cycle. If hardware development wereto slow incentives to avoid bloat would emerge, and even for non‐bloating software applications the benefits ofbuying new hardware often would lessen. This would reduce the strength of the feedback loop (and possibly removeit), severely reducing the increase in computing capabilities.

100Appendix C: Large‐scale neural network
simulations
These simulations represent a small sampling of the literature. There is a bias towards
research aimed at high performance computational neuroscience and developing new
methods.
Table 11: Large‐scale neural simulations
Simulation

Type ofsimulation

Neurons

Synapses

HardwareandSoftware(Plesser, Eppler etal., 2007)

Integrate-andfire

12,5001.56∙107(Djurfeldt,
Lundqvist et al.,2006)

6-compartmentneurons

2.2∙1071.1∙1010

Sun X4100
cluster,
2.4Ghz AMD
Opteron,
8GB ram,
MPI, NESTIBM Warson
ResearchBlue Gene,
SPLIT(Izhikevich, 2005)

Izhikevich
neurons,
Randomsynaptic
connectivity
generated on thefly.
Compartment(Purkinje 4,500
comp./granule 1
comp.)

1011

1015(Howell,Dyhrfjeld-Johnsen
et al., 2000)(Howell,Dyhrfjeld-Johnsen
et al., 2000)(Kozlov, Lansner
et al., 2007)(Frye,
Ananthanarayanan
et al., 2007)(Brette, Rudolph etal., 2007; Traub,
Contreras et al.,2005)
(Traub, Contreras
et al., 2005)

5 compartment
model
Low complexity
spiking likeIzhikevich
neurons, STDP
synapses
14 cell types,
conductance andcompartment
model
14 cell types,
conductance andcompartment
model, 11 active
conductances

16 Purkinje,
244,000
granule cells
60,000
granule cells,
300 mossy
fibers, 300
Golgi cells,
300 stellate
cells, 1
Purkinje cell
900
8∙106

3,560

3,560

10% perhemisegment
6300*8∙106=
5.04∙1010

3,500 gap
junctions,
1,122,520
synapses
3,500 gap
junctions,
1,122,520
synapses

Slowdown(timerequired for
1 biologicalsecond)2

4.2∙106

128processor
Cray 3TE,
PGENESIS
Singleworkstation
1GB
memory,GENESIS

4,500

79,200SPLIT

14 cpu Linuxcluster, an
IBM e1350,
dualprocessor
Intel P4 Xeon

Notes

0.1 msSupralinear
scaling until 80
virtualprocesses for
105 neuron/109
synapses.
Computingrequirements:
11.5 TFLOPS.
Spatial delays
corresponding
16 cm2 cortex
surface.

5,942

Beowulf
cluster, 27
3GHz
processors

4096
processor
BlueGene/L
256 Mb per
CPU
Cray XT3 2.4
Ghz, 800
CPUs

Timestep

Computingrequirements:
76.8 GFLOPS
20 µs

100 µs
10

1 ms

200?

Event
driven5954-8516
equations/CPU
Gives #
compartments

2 µs

101

(Goodman,
Courtenay Wilson
et al., 2001)(Frye, 2004)(Aberdeen, Baxter
et al., 2000)(Kondo, Koshiba etal., 1996)(Ccortex, 2003)(Harris, Baurick etal., 2002)(Mehrtash, Jung etal., 2003)(Shaojuan andHammarstrom,
2002)(Johansson andLansner, 2007)

2.4-GHz, 512
Kbytes of L2
cache; or,e1350 Blade1, at 2.8 GHz
NCS, 30
dualPentium III
1GHz
processor
nodes, with 4
GB of RAMper node

5,150 pernode, max
20*5,150=103,000

In columns
192,585 pernode, max 20= 3.85∙106,
intercolumn
1.4∙106

1,000/cells
column,2,501
columns =2.5∙106

250K percolumn, total
6.763∙108

BlueGene,
1024 CPUs

4,083

1.73∙1061,536

2.4∙106

Layereddistribution ofneural nets,
detailed synaptic
interconnections,spiking
dynamics
Point neurons,
spikes, AHP, A& M potassiumchannels,
synaptic
adaptation,
Spike shape andpostsynaptic
conductancespecified bytemplates.
Integrate andfire, event
driven pulse
connections with
STDP plasticity.
1-8 dendritic
segments.
Palm binary
network

2∙10102∙101335,000 cells
per node

6.1∙106
synapses per
node

Cluster 196
pentium III
processors,
550 MHz,
384Mb
Specialpurposechips
500 nodes,
1,000
processors, 1
Tb RAM.
Theoreticalpeak of 4,800
Gflops.
Cluster, 128
Xeon 2.2
GHz
processors
with 256GB,
1Tb disk

256,000

106

256,000

Order of
3.2∙1010

BCPNN spiking,minicolumns
hypercolumns

1.6∙106

2.0∙1011

3 layer corticalcolumn, 25%
GABAergic,
conductancemodels, synapse
reversal pot.,
conductance,abs. str., meanprob. release,time const.
recover depress
facilitation,
Four cell types.
Km, Ka, Kahp
channelsANN
feedforward
network trainedwith iterative
gradient descent
Hopfield
networkSpecialpurposechip,
connected
UltraSparc
processor 500
Mhz, 640 Mb
512
processors,
192 GB, 250
Mhz mips
12K
processorr

256 node, 512
processors,
Dell Xeon
cluster,

165,000

3,000?

2.338∙1010
spikes perprocessor.
Memoryrequirement
110 Mb perprocessor

Claimedperformance,notdocumented.

1012.21timessteps/
ms

Execution
time 4 min 7
sec, -13% MPI
calls forretrieving
180K training
vectors,~O(10)
iterations
9% real-time= 11.1

Weightupdates 47 ms,
updateactivities 59 ms

102

(Markram, 2006)(Traub and Wong,
1982)(Traub, Miles et al.,
1992)
(Moll andMiikkulainen,
1997)

Morphologicallycomplexcompartment
models

10,000108

120 kW usage.
Computational
demand256*13.6 Gflops

3.4GHz 8 Gb
memory,peak
performance
13.6 Gflop
BlueGene/L,4096 nodes,
8192 cpus,
peakperformances
22.4 TFLOPS

Not
documented?

100
19 compartment
cells, ioniccurrents
Binary units
with binary
weights

1,200

70,000IBM3090
computer

79,5007.82∙108

Cray Y-MP
8/864

325 min/1.5 s
bio time =13,0000.006 s(retrieval of
550,000
patterns;
assuming
humanretrieval
speed = 1 s)

0.05 ms-

Plotting the size of the simulations over time does suggest a rapidly increasing scale,although there is not enough data to estimate a reliable trend. There does not seem to exist aclear separation between biologically detailed models and highly simplified models; some of
the largest simulations have been detailed compartment models.

Figure 36: Number of neurons in different large computational neuroscience models.

103

Figure 37: Number of synapses in different computational neuroscience models.

104

Appendix D: History and previous work
The earliest origins of the mind emulation idea can perhaps be traced back to J.D. Bernal’s The
World, The Flesh, The Devil (1929), where he wrote:“Men will not be content to manufacture life: they will want to improve on it. For one
material out of which nature has been forced to make life, man will have a thousand;living and organized material will be as much at the call of the mechanized orcompound man as metals are to‐day, and gradually this living material will come to
substitute more and more for such inferior functions of the brain as memory, reflex
actions, etc., in the compound man himself; for bodies at this time would be left far
behind. The brain itself would become more and more separated into different
groups of cells or individual cells with complicated connections, and probably
occupying considerable space. This would mean loss of motility which would not be
a disadvantage owing to the extension of the sense faculties. Every part would not be
accessible for replacing or repairing and this would in itself ensure a practical eternityof existence, for even the replacement of a previously organic brain‐cell by a synthetic
apparatus would not destroy the continuity of consciousness.
…
Finally, consciousness itself may end or vanish in a humanity that has becomecompletely etherealized, losing the close‐knit organism, becoming masses of atoms inspace communicating by radiation, and ultimately perhaps resolving itself entirely
into light.“
Bernal’s vision corresponds to a gradual replacement of biology with artificial parts,
gradually making it unnecessary to keep the brain in one location.
In the science fiction novel City and the Stars (1956) Arthur C. Clarke described a far futurecity where bodies are manufactured by the central computer, minds stored in its databanks
downloaded into them, and when an inhabitant dies their mind is stored yet again in thecomputer, allowing countless reincarnations. Other early science fiction treatments were
Roger Zelanzky’s Lord of Light (1968), Bertil Mårtensson’s Detta är verkligheten (1968) andRudy Rucker’s Software (1979). Since then, mind emulation (“uploading”) has become a stapleof much science fiction23. Of particular note in terms of technological and philosophicaldetails are the novels and short stories by Greg Egan (Permutation City, Diaspora, Learning to be
Me, Transition Dreams etc).
Brain (and mind) emulation has also been widely discussed in philosophy of mind, althoughmore as Gedankenexperimente than possible actual practice (e.g. (Parfit, 1984; Chalmers, 1995;
Searle, 1980)).
The first attempt at a careful analysis of brain emulation was a technical report (Merkle,
1989b) predicting that “a complete analysis of the cellular connectivity of a structure as large
as the human brain is only a few decades away”. The report reviewed automated analysis
and reconstruction methods, going into great detail on the requirements needed for parallel
processing of brain samples using electron microscopes and image analysis software. It also
clearly listed assumptions and requirements, a good example of falsifiable design.

23

E.g. http://en.wikipedia.org/wiki/Mind_transfer_in_fiction

105

The first popularization of a technical description of a possible mind emulation scenario wasfound in Hans Moravec’s Mind Children (1990), where the author describes the gradualneuron‐by‐neuron replacement of a (conscious) brain with software. Other forms ofemulation are also discussed.
(Hanson, 1994) was the first look at the economical impact of copyable minds, showing thatsocial role‐fit brain emulation would likely cause dramatic economic and demographic
changes .
One sketch of a person emulation scenario (Leitl, 1995) starts out by the cryonic suspension of
the brain, which is then divided into cubic blocks < 1mm. The blocks can individually bethawed for immunostaining or other contrast enhancement. For scanning, various methods
are proposed: X‐ray fresnel/holographic diffraction, X‐ray or neutron beam tomography (allrisking radiation damage, might require strong staining), transmission EM (requires very thinsamples), UV‐abrasion of immunostained tissue with mass spectrometry, or abrasive atomic
force microscope scan. While detailed in terms of the cryosuspension methods, the sketch
becomes less detailed in terms of actual scanning method and implementing the emulation.

106

Appendix E: Non‐destructive and gradualreplacement
Non-Destructive Scanning
Non‐destructive scanning requires minimally invasive methods. The scanning needs toacquire the relevant information at the necessary 3D resolution. There are however several
serious limitations:
•

•
•
•

The movement of biological tissue, requiring either imaging faster than it can moveor accurate tracking. In cats, the arterial pulse produces 110–266 μm movements
lasting 330–400 ms and breathing larger (300–950 μm) movements (Britt and Rossi,1982)24. The stability time is as short as 5‐20 ms.
Imaging has to occur over a distance of >150 mm (the width of an intact brain) or beinvasive.
The imaging must not deposit enough energy (or use dyes, tracers or contrast
enhancers) to hurt the organism.
The method must not significantly alter the mental or neural state of the subject being
scanned in order to avoid a possibly significant “observer effect” anomalies and false
reading that could produce a flawed emulation model.

Of the possible non‐invasive candidates only MRI appears able to fulfil the limitations even in
principle. Optic imaging, even using first‐arriving light methods, would not work across sucha large distance. X‐ray tomography of the intensity needed to image tissue would deposit
harmful energy (see discussion on X‐ray microscopy).
The resolution of MRI depends on the number of phase steps used, gradient strength,acquisition time and desired signal‐to‐noise ratio. To record micron‐scale features in amoving brain, very short acquisition times are needed, or a way of removing the movement
artefacts. Each doubling of spatial resolution divides the signal‐to‐noise ratio by 8, requiring
longer acquisition times, stronger fields or more sensitive detectors. Finally, there are also
problems with tissue water self‐diffusion, making resolutions smaller than 7.7 μm impossible
to achieve (Glover and Mansfield, 2002)25. Given that brain emulation on the synaptic level
requires higher resolution, this probably rules out MRI as a non‐destructive scanning method.
However, if the brain is frozen, water diffusion and movement do not occur and very longacquisition times can be used. One problem with MRI of frozen brain tissue is that limited
proton mobility reduces the signal; this can be ameliorated by keeping the brain at ‐1oC
(Longson, Hutchinson et al., 1995; Doyle, Longson et al., 1996). MRI might therefore be apossible scanning method for frozen or fixed brains. Since it is not destructive to the tissue it
may also act as an adjunct to other, destructive, scanning methods.

The authors of the paper suggests as a solution using a cardiac bypass system to create a nonpulsative
flow of oxygenated blood.

24

25 Higher resolutions have been reported based on using micro‐coil receivers and very strong magnetic field
gradients. (Ciobanu, Seeber et al., 2002) reports a resolution of 3.7 x 3.3 x 3.3 μm, but the imaging time was 30h andthe authors were pessimistic about the ability to detect different chemicals.

107

A possible way around the problems of non‐invasive scanning would be to use endoscopic
methods to bring measurement devices into the blood vessels of the brain and image it frominside. Endoscopic MRI has been demonstrated in the gastrointestinal system where a RF coil
was affixed to the tip of an endoscope (Inui, Nakazawa et al., 1998). This is limited by therange of the devices, their size and the risks of penetrating vessels. However, it appears
unlikely that all parts of the brain are close enough to a large blood vessel to allow accurate
scanning, even leaving out the resolution and movement issues.
Correlation mapping using nanoprobes (Strout, 2006) has also been suggested. A large
number (1012) of nanomachines set up residence in or on neurons, recording their activity andfunctional correlations. A more detailed analysis of how nanomachines could sense neuralactivity can be found in (Freitas Jr, 1999, section 4.8.6) together with a sketch of an in vivo
fibre network with 1018 bit/s capacity (Freitas Jr, 1999, section 7.3.1). This would enable
essentially real‐time monitoring of all neurons and their chemical environment (Kurzweil,
2005, pp. 548‐549). Whether just the activity would be enough to map the connectivity andmemory states of the brain is unclear. However, given the assumed technology both
intracellular probing and physical connectivity mapping by histonating nanorobots appears
possible. Whether this would interfere with brain function is hard to estimate (but see (Freitas
Jr., 2003) for an analysis). The fibre network would have a volume of 30 cm3, which is ≈41% of
the normal blood volume in the brain (based on estimates of blood volume in (Leenders,
Perani et al., 1990)), possibly impeding bloodflow if extended inside vessels. Although thecapabilities of nanomachines can be constrained by known physics it is not possible today toinfer enough about machine/cell/tissue interactions to estimate whether non‐destructive
scanning is feasible.
Overall, the prospects for non‐destructive scanning do not look good at present. At the very
least it would need to involve very invasive endoscopic scanning or the use of advanced
nanomedicine.

Gradual replacement
Scanning might also occur in the form of gradual replacement, as piece after piece of the brain
is replaced by an artificial neural system interfacing with the brain and maintaining the samefunctional interactions as the lost pieces. Eventually only the artificial system remains, andthe information stored can be moved if desired (Morevec, 1988). While gradual replacement
might assuage fears of loss of consciousness and identity26 it appears technically very
complex as the scanning system not only has to scan a living, changing organism but alsointerface seamlessly with it (at least on the submicron scale) while working. The technology
needed to achieve it could definitely be used for scanning by disassembly. Gradualreplacement is therefore not likely as a first form of brain emulation scanning (though inpractice it may eventually become the preferred method if non‐destructive scanning is notpossible).
It is sometimes suggested that extending the brain through interfaces with external software
might achieve a form of transfer where more and more of the entire person is stored outside
the brain, possibly reaching the point where the brain is no longer essential for the composite
person. However, this would not be brain emulation per se but rather a transition to a

26 But not necessarily. Searle has argued that replacement would gradually remove conscious experience (Searle,1980). Parfit’s ‘physical spectrum’ thought experiment involves interpolating between two different people thatclearly have different identities, and the replacement process could have a similar property (Parfit, 1984).

108

posthuman state. The technical feasibility appears ill‐defined given current knowledge. It
should be noted that even relatively partial such interfaces or life recording (Gemmell, Bell etal., 2006) would produce a wealth of useful data for developing brain emulations by acting asa yardstick of normal function27.

27 Again, it is sometimes suggested that recording enough of the sensory experiences and actions would be enough to
produce brain emulation. This is unlikely to work simply because of the discrepancy in the number of degrees of
freedom between the brain (at least 1014 synaptic strengths distributed in a 1022 element connectivity matrix) and thenumber of bits recorded across a lifetime (less than 2∙1014 bits (Gemmell, Bell et al., 2006)).

109

Appendix F: Glossary
AFM
ANN
ATLUM
Autoregulation
Axon
BlockfaceboutonCNS
Confocal microscopy
Connectome
Dendrite
Exaflop
Extracellular
FIBSEM
FLOPS
Fluorophore

FPGA

GABAergic
GFLOP
Glia
Hypercolumn
Interneuron
KinaseLigandMetabolome
MFLOPS
Microtubule

Atomic force microscope (sometimes called scanning force
microscope).
Artificial Neural Network, a mathematical model based on
biological neural networks.
Automatic Tape‐Collecting Lathe Ultramicrotome
Regulation of blood flow to maintain the (cerebral) environment.
Projection from a nervcell that conducts signals away from theneuron’s cell body.
The surface of an imaged sample, especially when cutting takes
place.
The typical synaptic bump that grows between axons anddendrites.
Central Nervous System.
Optical imaging technique to image 3D samples by making useof a spatial pinhole.
The total set of connections between regions or neurons in abrain (Sporns, Tononi et al., 2005).
Branched projections of neurons that conduct signals from otherneurons to the cell body.
1018 FLOPS.
The environment outside the cells.
Focused Ion Beam SEM.
Floating point Operations Per Second. A measure of computing
speed.
A molecule or part of molecule that causes fluorescence whenirradiated by UV light. Used for staining microscope
preparations.
Field‐Programmable Gate Array. Semiconductor device that
contains programmable logic and interconnects, allowing thesystem designer to set up the chip for different purposes.
Related to the transmission or reception of GABA, the chief
inhibitory neurotransmitter.
Gigaflop, a billion FLOPS.
Glia cells are non‐neuronal cells that provide support, nutrition
and many other functions in the nervous system.
A group of cortical minicolumns organised into a module with afull set of values for a given set of receptive field parameters.
In the CNS, a small locally projecting neuron (unlike neurons
that project to long‐range targets).
An enzyme that phosphorylates other molecules.
A molecule that bonds to another molecule, such as a receptor or
enzyme.
The complete set of small‐molecule metabolites that can be foundin an organism.
Millions of Floating point Operations Per Second. A measure ofcomputing speed.
A component of the cell skeleton, composed of smaller subunits.

110Minicolumn

MIPS
Motorneuron
MRI
Neocortex

NeuriteNeurogenesis
Neuromodulator
Neuromorphic
NeuronNeuropeptide
Neurotransmitter
Parallelisation
PCR
Petaflop
Phosphorylation

PNS
Potentiation
SBFSEM
SEM
Sigmoidal

Skeletonization

Soma
Spectromicrosopy
SSET
SSTEM
Supervenience

SynapseSynaptic spine

A vertical column through the cerebral cortex; a physiologicalminicolumn is a collection of about 100 interconnected neurons, afunctional minicolumn consists of all neurons that share thesame receptive field.
Millions of Instructions Per Second. A measure of computing
speed.
A neuron involved in generating muscle movement.
Magnetic Resonance Imaging.
The cerebral cortex, covering the cerebral hemispheres.
Neocortex distinguishes it from related but somewhat more“primitive” cortex that have fewer than six layers.
A projection from the cell body of a neuron, in particular from adeveloping neuron where it may become an axon or dendrite.
The process by which neurons are created from progenitor cells.
A substance that affects the signalling behaviour of a neuron.
Technology aiming at mimicking neurobiological architectures.
A nerve cell.
A neurotransmitter that consists of a peptide (amino acid chain).
A chemical that relays, amplifies or modulates signals fromneurons to a target cell (such as another neuron).
The use of multiple processors to perform large computations
faster.
Polymerase Chain Reaction, a technique for amplifying DNA
from a small sample.
1015 FLOPS.
Addition of a phosphate group to a protein molecule (or othermolecule), usually done by a kinase. This can activate ordeactivate the molecule and plays an important role in internalcell signalling.
Peripheral Nervous System.
The increase in synaptic response strength seen after repeatedstimulation.
Serial Block‐Face Scanning Electron Microscopy
Scanning Electron Microscopy.
S‐shaped, usually denoting a mathematical function that ismonotonously increasing, has two horizontal asymptotes andexactly one inflection point.
Image processing method where a shape is reduced to the set ofpoints equidistant from its boundaries, representing its
topological structure.
The cell body of a neuron.
Methods of making spectrographic measures of chemicalcomposition in microscopy.
Serial Section Electron Tomography
Serial Section Transmission Electron Microscopy
A set of properties A supervenes on a set of properties B if andonly if any two objects x and y that share all their B properties
must also share all their A properties. Being B‐indiscernible
implies being A‐indiscernible.
A junction where one (or more) neurons signal to each other.
Many synapses have their boutons offset from their parent

111

TEM
TFLOPS
Tortuosity
V1
Voxel

dendrite through a thinner filament.
Transmission Electron Microscopy.
Teraflops, 1012 FLOPS.
A measure of how many turns a surface or curve make.
The primary visual cortex.
A volume element, representing a value on a regular grid in 3D
space.

112

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