The Scientific Case for Brain Simulations
Authors: Gaute T Einevoll
Michele Migliore
Hans E Plesser
Torbjørn V Ness
Viktor Jirsa
Alain Destexhe
Markus Diesmann
Marc de Kamps
Felix Schürmann
Sonja Grün
Journal: Neuron (2019)
Discipline: NEUROSCIENCE & BEHAVIOR
Keywords: brain simulation; model; network; neuron; simulation
Affiliations: Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway; Department of Physics, University of Oslo, 0316 Oslo, Norway. Electronic address: ., Paris-Saclay Institute of Neuroscience (NeuroPSI), Centre National de la Recherche Scientifique, 91198 Gif-sur-Yvette, France; European Institute for Theoretical Neuroscience, 75012 Paris, France., Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), and JARA-Institut Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52425 Jülich, Germany; Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany; Department of Physics, RWTH Aachen University, 52074 Aachen, Germany., Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), and JARA-Institut Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52425 Jülich, Germany; Theoretical Systems Neurobiology, RWTH Aachen University, 52074 Aachen, Germany., Institut de Neurosciences des Systèmes (INS), INSERM, Aix Marseille Université, 13005 Marseille, France., Institute for Artificial and Biological Intelligence, School of Computing, Leeds LS2 9JT, UK., Institute of Biophysics, National Research Council, 90146 Palermo, Italy., Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway., Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway; Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), and JARA-Institut Brain Structure-Function Relationships (INM-10), Jülich Research Centre, 52425 Jülich, Germany., Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland.

Abstract:  A key element of the European Union's Human Brain Project (HBP) and other large-scale brain research projects is the simulation of large-scale model networks of neurons. Here, we argue why such simulations will likely be indispensable for bridging the scales between the neuron and system levels in the brain, and why a set of brain simulators based on neuron models at different levels of biological detail should therefore be developed. To allow for systematic refinement of candidate network models by comparison with experiments, the simulations should be multimodal in the sense that they should predict not only action potentials, but also electric, magnetic, and optical signals measured at the population and system levels. 

Retracted: No

PubPeer comments:
 Far from presenting a u201cscientific case,u201d these participants Human Brain Project u2013 already the beneficiary of a billion of Euros - throw together a bunch of words and then ask for money u2013 and lots of it u2013 from European taxpayers. That they are interested parties is mentioned at a random point in the text u2013 it should have been placed at the end, where such disclosures are usually found. Einevoll et al make no case u2013 the awkward qualifier u201cscientificu201d in the title indicates to me a cosmetic attempt to rebut existing criticism that the Project does not have scientific value - or at least not enough of it to justify its contnued massive funding. (Really, if participants are just now attempting to frame a case for the sole purpose of the HBP, how was the original funding justified?) Either this article is a. not intended to be read beyond the headline, b. the reading public is considered severely deficient in crtitical thinking skills, or c. it doesn t matter what the reading and paying public thinks, because the bureaucrats and oligarchs will do anything they want and articles like this are just a fig leaf. Judging from a recent comment by Henry Markram in an Atlantic article discussing how the HBP has failed to meet its goal, it s option  c :  Political resistance (non-scientific) to the project has indeed slowed us down considerably, but it has by no means stopped us nor will it. Maybe you have to be looking for it, but a bright red flag goes up in the abstract, where the authors state u201cwe argue why such simulations will likely be indispensible for bridging the scales between the neuron and system levels in the brain...u201d Will likely be? They re not sure? And, likely to do what? Bridge scales? The implication is that simulations or other methods have already proven useful for understanding what s going in with a given u201cscale,u201d and we just need to u201cbridgeu201d these scales. Sloppy and biased as it is, the text itself makes amply clear that this is not the case. Why, the very first sentence acknowledges that: u201cDespite decades of intense research efforts investigating the brain at the molecular, cell, circuit, and system levels, the operating principles of the human brain, or any brain, remain largely unknown. Likewise, effective treatments for prevalent serious psychiatric disorders and dementia are still lacking.u201d Next, a weak effort to claim some ground, which is itself groundless: u201cIn broad terms, one could argue that we now have a fairly good understanding of how individual neurons operate and process information but that the behavior of networks of such neurons is poorly understood.u201d In broad terms one could argue we have a fairly good understanding... No, one really couldn t. The basis of such claims is given as: u201cthe pioneering work of Hubel and Wiesel (1959) in mapping out receptive fields in the early visual system,u201d subsequent to which u201csimilar approaches have been used to explore how different types of sensory input and behavior are represented in the brain.u201d So: Even though the behavior of u201cnetworks of neuronsu201d is u201cpoorly understood,u201d researchers have managed to u201cexploreu201d (presumably with some success) how u201csensory input and behavior are *represented* in the brain.u201d Representation apparently doesn t require networks, only individual neurons! The unscientific basis of such contradictory and paradoxical assertions becomes more clear in the following passage. u201cIn these projects, the statistical correlation between recorded neural activity, typically action potentials from single neurons, and sensory stimulation or behavior of the animal is computed. From this, so-called descriptive mathematical models have been derived, accounting for, say, how the firing rate of a neuron in the visual system *depends on* the visual stimulus. The qualitative insights gained by obtaining these descriptive receptive-field models should not be underestimated, but these models offer little insight into how networks of neurons give rise to the observed neural representations. Such insight will require mechanistic modeling in which neurons are explicitly modeled and connected in networks.u201d Projects measuring u201cthe statistical correlation between recorded neural activity...and sensory stimulation or behavioru201d are post hoc correlation-fishing exercises based on a very simplistic and very confounded u201cstimulus-responseu201d concept of neural function. The resulting u201cassociationsu201d are [universally acknowledged] to be non-replicable. Despite this, authors publishing this type of data routinely (as noted by Mehler and Kording (2018)) employ causal language u2013 as in claiming u201cthe firing rate of a neuron *depends on* the visual stimulus . The term u201cmodel,u201d the correlation-fished data-fitting exercise counterpart to u201chypothesisu201d is used to denote these non-replicable post hoc associations. (I ve compiled a partial list of u201cNeuroscience Newspeaku201d terms [here] ). Thus, the u201cso-called descriptive mathematical models...accounting for [read: correlating], say, how the firing rate of a neuron in the visual system depends [was correlated in a particular confounded measurement] on the visual stimulusu201d possess no theoretical value, and it really is not possible to u201cunderestimateu201d their value. Don t let the authors more specific reference to u201cdescriptive receptive field modelsu201d fool you. As I ve pointed out in a number of comments, including [one] on a recent Science article, every u201creceptive fieldu201d evaluation is an illegitimate, ad hoc correlation-fishing expedition. The term has no agreed-upon definition, because it is not a valid concept. There is no circumscribed section of the retina, in other words, upon whose activity the activity of any particular visual neuron exclusively depends u2013 not even the neurons in the retina itself. The notion, which I believe originated with Kuffler in the fifties, the baton then taken up by Hubel and Wiesel, never held up, which is why it is now often referred to as the u201cclassical receptive field.u201d The substitute term, u201cnon-classical receptive field,u201d is similarly undefined. Even Kuffler (1953) understood that u201cu2026 not only the areas from which responses can actually be set up by retinal illumination may be included in a definition of the receptive field but also all areas which show a functional connection, by an inhibitory or excitatory effect on a ganglion cell.u201d Translation: Local effects on neural activity - whether a neuron will fire more or less or remain the same - are contingent on what is happening in the entire retina. But the simplicity of the receptive field concept, combined with the intellectual laziness of practitioners, have ensured it remains core value in neuroscience. In case the situation was not clear enough, we may move on to the section entitled u201cHypothesis underlying brain network simulators.u201d We begin, as usual, with an admission of profound ignorance: u201cAt present, we do not have a well-grounded, and certainly no generally accepted, theory about how networks of millions or billions of neurons work together to provide salient brain functions ni animals or humans. We do not even have a well-established model for how neurons in the primary visual cortex of mammals work together to form the intriguing neuronal representations with, for example, the orientation selectivity and direction selectivity that were discovered by Hubel and Wiesel 60 years ago (Hubel and Wiesel, 1959).u201d Hubel and Wiesel s (1959) tentative claims haven t held up. This is why they are constantly being cited, rather than something more recent. As mentioned, the u201creceptive fieldu201d concept was always a non-starter, and the simple complex dichotomy has similarly failed to hold up (see, for example, discussion in Carandini et al (2005). All u201cselectivityu201d claims to data are based on the above-mentioned, non-replicable correlation-fishing exercises, some, like DiPoppa, Ranson, Krumin, Pachitariu, Carandini and Harris (2018) [using plain-vanilla p-hacking] . After these cathartic admissions, the authors pin their argument on this: u201cHowever we do know the biophysical principles of how to model electrical activity in neurons and how neurons integrate synaptic inputs from other neurons and generate action potentials.u201d Is this true? u201cThese principles, which go back to the work of Hodgkin and Huxley (1952) and Rall (Segev et al., 1994) and are described in numerous textbooks [!]...are the only hypotheses underlying the construction of brain network simulators.u201d Note that the only primary citations provided supporting the claims for what u201cwe do knowu201d about neural function are, again, both from the fifties. With respect to Rall (from Scholarpedia): u201cThe importance of the cable properties of dendrites was emphasized in 1957, when Wilfrid Rall corrected erroneous estimates of the membrane time constant of motoneurons in cat spinal cord. During the 1960s, cable models (a cylinder of finite length, and a ten-compartment model) were used to predict experimental results that were successfully tested with research collaborators at the National Institutes of Health. This research established the significance of synaptic input to distal dendritic locations; it also explored properties of dendritic spines, and predicted dendro-dendritic synapses in the olfactory bulb.u201d Thus, the HPB is apparently based on research on neurons done in the fifties, and on references to this work in textbooks. As in the case of u201cneuron selectivity,u201d some early, tentative, immature observations which researchers have apparently failed to flesh out or which have failed to produce a coherent, follow-up body of work are to be taken as carved in stone and used as a pretext for endless u201cmodellingu201d exercises relying on correlation-fished data. People who have studied the issues with greater seriousness than that exhibited by the traditions described here have produced extremely cogent arguments showing why neural function and its products cannot be based on the principles that govern machines such as computers. We are not yet in a position to create a single living cell; we re thus not even close to creating a u201cmachineu201d that exhibits the dynamic properties of the living brain. Even if one wanted to argue that computers could eventually simulate the brain, this would entail responsible counterarguments rebutting the well-rationalized opposing views. Responsible counterarguments, in turn, would entail an understanding of what it is the brain actually achieves. The visual process is one of the main areas of interest in neuroscience. I m painfully aware of the fact that the electrical engineers, software designers, and statisticians currently passing for neuroscientists have never spent a moment considering the nature of the problems vision solves, nor the underlying physiology. If they had, they would have known in advance that the current [failed], statistical AI approach was doomed from the start. They would have known that an elementist view u2013 that brain or any living function, or even complex inorganic processes - can be modelled by individual elements interacting by addition, subtraction, multiplication or division is not consistent with the dynamical structuring implicated in function. And modelled based on primitive, 60-year-old understanding of those elements at that. The authors observe that: u201cAction potentials are clearly the key carrier of information but what aspects of the trains of action potentials should be mimicked by brain simulations?u201d How can trains of spikes, clearly orders of magnitude simpler than the contents of perception and thought, be the u201ckey carrieru201d of the information underlying these experiences? The authors also specify that: u201cTo construct candidate network models, information on the...spatial positions of neurons, as well as information on how these neurons are connected, are needed.u201d Again, when the u201cspatial positionsu201d and connectivity of actual neurons are so fluid and dynamical u2013 when we can lose individual neurons, blocks of neurons, even a whole hemisphere and recover function - the idea that u201cspatialu201d information may even be meaningfully gathered seems to indicate a fundamental lack of understanding of the nature of brain function. It is a static machine view. The authors of this article do seem to have a vague understanding of the dynamical nature of the problem u2013 they use as their proof of concept simulations of weather patterns. Obviously, despite its complexity, the field of meteorology is orders of magnitude less complex than the brain; and success has been distinctly limited; and of course, meteorologists, too, are pinning their hopes on ever bigger computers: u201cA more fundamental problem lies in the chaotic nature of the partial differential equations that govern the atmosphere. It is impossible to solve these equations exactly, and small errors grow with time (doubling about every five days). Present understanding is that this chaotic behavior limits accurate forecasts to about 14 days even with perfectly accurate input data and a flawless model.u201d (Wikipedia) u201cSome meteorological processes are too small-scale or too complex to be explicitly included in numerical weather prediction models.u201d (ibid) u201cAdvances in HPC have brought the global weather and climate prediction communities to the point where it is possible to replace various parametrizations of crucial physical processes by fluid-dynamical representations based on fundamental principles...Simulations at this scale, with the required throughput, are becoming imaginable, given radically new algorithmic approaches and dedicated software optimization of in-production-use weather and climate models and the rise of actual exascale supercomputers.u201d [Neumann et al 2019] So even weather simulations are in the u201cimaginable using radically new algorithmic approachesu201d stage. In another salvo, the authors tell us that their supercomputers will function as basic tools, much like Newton s calculus. In making this rather childlike argument, they show their lack of understanding of scientific practice and the logic of discovery. u201cTo test the validity of hypotheses [of gravitation], Newton had to compare them with the available measurements of planetary orbits. However, the connection between the mathematically formulated hypotheses and the shapes of predicted planetary orbits is not obvious. In fact, Newton developed a new type of mathematics, calculus, to make testable predictions from his theory [of gravitation]...However, if it had turned out that the predictions of planetary orbits were not in accordance with the observational data, Newton could have tried other mathematically formulated hypotheses...u201d First, looking into it, I don t think it s accurate to say that Newton invented calculus in order to prove his hyptheses. For example: u201cOver two miraculous years, during the time of the Great Plague of 1665-6, the young Newton developed a new theory of light, discovered and quantified gravitation, and pioneered a revolutionary new approach to mathematics: infinitesimal calculus.u201d And it appears that people, e.g.Kepler, measuring planetary orbits prior to Newton were already pretty good at it: u201cThis rigorous intuitive stage-by-stage comparison and summing-up of the areas to find orbitu2019s using the adaption of geometry marked the beginning of a technique called u2018calculus of indivisiblesu2019, a lengthy analytical pre-cursor to Newtonian calculus. All of Kepleru2019s proofs for his laws are founded on geometrical facts and extensive numerical data, and therefore donu2019t provide deductive proofs to his own laws. It was later that century that Newton was able to provide deductive proofs to all of Kepleru2019s Laws, establishing that they follow mathematically from his own Laws of Gravitation and Laws of Motion.u201d Thus, Newton s theory appears to have been consistent with, and in a sense to have been corroborated by, existing evaluations of planetary motion. Second, the idea that if his hard-won theories had failed, Newton could simply have tried out other mathematically-formulated hypothesesu201d - crapshoot-style - speaks to the degeneracy of the current understanding of what it takes to formulate a scientific theory, specifically an understanding of the conceptual work involved. , 
 While awaiting moderation on a previous comment noting the many fundamental weaknesses of Einevoll et al s  scientific case for major, ongoing funding for the Human Brain Project, ( I don t think I ve ever had a comment pre-emptively flagged for moderation before) I will add that the authors never address the project s well-known failure to live up to its promises, as described, for example, in an [Atlantic article] which came out two months after this publication. From the start the program has tried to wave off critics from within the field, but after an open letter from neuroscientists saying, among other things, that they believed  the HBP is not a well conceived or implemented project and that it is ill suited to be the centerpiece of European neuroscience, a mediation committee [called for a major overhaul](http: nautil.us blog the-big-problem-with-big-science-ventureslike-the-human-brain-project) in 2015. It would seem appropriate for Einevoll et al to have in some way indicated their awareness of colleagues objections, of the failures, of the reasons for the failures, and provided some form of rebuttal. The Atlantic article provides an example of a major objection to the case Einevoll are claiming to make:  [Markram] pitched it as an experimental test bedu2014a way for scientists to test their hypotheses without having to prod an animalu2019s head. u201cThat would be incredibly valuable,u201d [Grace] Lindsay says, but itu2019s *based on circular logic.* A simulation might well allow researchers to test ideas about the brain, but those ideas would already have to be very advanced to pull off the simulation in the first place. u201cOnce neuroscience is u2018finished,u2019 we should be able to do it, but to have it as an intermediate step along the way seems difficult.u201d It s the neuroscience, stupid. It s a bad sign if the survival of the HBP depends on turning a blind eye to criticism, or dismissing it as  political resistance, as in the Markram quote in my earlier comment. ,