ViewpoinT
Reflections on the past two decadesof neuroscience
Danielle S. Bassett , Kathleen E. Cullen , Simon B. Eickhoff ,Martha J. Farah , Yukiko Goda , Patrick Haggard , Hailan Hu ,Yasmin L. Hurd , Sheena A. Josselyn , Baljit S. Khakh , Jürgen A. Knoblich ,Panayiota Poirazi , Russell A. Poldrack , Marco Prinz , Pieter R. Roelfsema ,Tara L. Spires-Jones , Mriganka Sur and Hiroki R. Ueda

Abstract | The first issue of Nature Reviews Neuroscience was published 20 years
ago, in 2000. To mark this anniversary, in this Viewpoint article we asked a selectionof researchers from across the field who have authored pieces published in thejournal in recent years for their thoughts on notable and interesting developments
in neuroscience, and particularly in their areas of the field, over the past two
decades. They also provide some thoughts on current lines of research andquestions that excite them.
Broadly, how has the field of neurosciencechanged over the past two decades, andwhat do you regard as the most notable orinteresting developments within your area of the
field over that period? What do you think are
the most interesting, big-picture questions being
asked in your area currently? Which lines ofresearch in your area of neuroscience are you
particularly excited about?
Danielle S. Bassett. Science is a culture
as much as it is a practice in the acquisitionand curation of knowledge. As time passes,
the field changes its culture and practice.
As a culture, neuroscience is now composedof a more diverse group of scientists, along
the dimensions of sex, gender, sexual
orientation, race, ethnicity and disability.
With the creation of anneslist (anneslist.
net), Women in Neuroscience Repository(winrepo.org) and BiasWatchNeuro(biaswatchneuro.com), there exist resources
to increase awareness of the work of womenscientists, and to encourage a greater balancein the speaker line-ups of conferences andsymposia. Yet explicit bias exists now as it
did in 2006 when transgender Professor
Ben A. Barres (Stanford University) wrote
his commentary for Nature entitled ‘Does
Gender Matter?’ And implicit bias is perhaps
even more pervasive, as reflected in peer
review, paper acceptance rates, grant funding
524 | October 2020 | volume 21

and a growing (rather than diminishing)undercitation of women in the reference listsof neuroscience journal articles in the past
20 years. Each of us as citizens of sciencecan choose to own and mould our culture
to realize a more equitable future.
As a practice in the acquisition andcuration of knowledge, neuroscience has
expanded upon prior paradigms. In humanneuroscience, I have been particularly struck
by a concerted effort to move beyond theactivation studies that were characteristic of
the brain-mapping era. Leading from thatpoint in scholarly space, two particularly
pioneering paths of formal study extend:
that of neural representations and that of
network systems. The former considers
the pattern of activity across neural units
that encodes an object, concept or state of
information. The latter considers the patternof connections between neural units that can
support the transmission of information andthe modulation of network state in a mannerthat alters the repertoire of computations
accessible to a given neural population.
The twin paths of representation andtransmission move beyond the mapping of acountry’s borders, to record a city’s statistics
and the transportation networks along
which traffic flows.
To me, the question that creates giant
resonance when dropped into the liquidmedium of conceptual space is: how do wecombine these twin paths to understandthe transmission of representations in the
network of the brain? Although answeringthat question will surely build on the priorparadigms, neither seems equipped toprovide a complete solution; the former
offers the dots within, whereas the latter
offers the lines between. Yet obtaining sucha solution is important for deciphering themechanism of the brain’s fundamental goal:
information processing. The efforts towards
such a solution that I find particularly
promising are those that move beyond
descriptive characterization or correlative
approaches, and towards theory; that is,theory instantiated in an interpretableand generalizable mathematical modelthat encodes a conceptual principle, andtheory as supported by (but not composedof) computational and machine-learning
approaches applied to data shared publicly
in the spirit of open science. Progress along
this tack will require more interdisciplinary
graduate training programmes that provide
equal depth in theory and experiment.
Kathleen E. Cullen. Traditionally, the fieldof neuroscience has asked the question: how
is sensory input (afference) transformed
into motor output (efference) to generate
appropriate behaviour? This conceptual
framework — initially made popular by
Sherrington — has long served as a common
foundation for systems neuroscientists.
A fundamental development over the past
two decades, however, has been a collective
realization that it is necessary to reverse thistraditional perspective. A series of recent
studies has established that motor signals
profoundly influence sensory processing
during voluntary behaviour. In particular,
experiments across sensory systems haveprovided circuit-level insight into how
motor-related inputs to sensory areas
selectively cancel self-generated sensory
inputs. This neural strategy underlies anessential property of the brain, namely its
ability to distinguish externally applied(exafferent) from self-generated (reafferent)
sensory inputs. For instance, work by mygroup has established that neurons at thefirst central stage of vestibular processing
preferentially encode sensory exafference.
Moreover, researchers studying otherwww.nature.com/nrn

Viewpoint
sensory pathways (for example, visual,
somatosensory and auditory) haveshown suppression of reafferent sensory
information by motor-related inputs
at higher levels of sensory processing,including in the cortex.
Indeed, much of the sensory inputthat we experience in our everyday lives
is reafferent as it is generated by our own
behaviour. The brain’s ability to predict
the sensory consequences of our actions

based on motor-related inputs is vital forboth perceptual stability and accurate
motor control. This computational strategy
also provides the flexibility required forfine-tuning and updating relationships
between motor signals and resultant
sensory feedback in order to compensate
for changes to our body or the environment.
Furthermore, the ability to distinguishbetween sensory exafference and reafferenceis a hallmark of higher-level perceptualand cognitive processing that is central to
our subjective awareness of initiating andcontrolling our own volitional actions in theworld (that is, our sense of agency). In thiscontext, a fundamental question logicallyarises: how exactly does the brain actually
predict the sensory consequences of ouractions in a dynamically changing world?
Two particularly exciting lines of research
are currently addressing this question.
First, emerging technologies including

The contributors
Danielle S. Bassett is the J. Peter Skirkanich Professor at the Universityof Pennsylvania, Departments of Bioengineering, Electrical and Systems
Engineering, Physics and Astronomy, Neurology, and Psychiatry. Her
expertise centres on the architecture, function, design and control ofcomplex systems, with a particular focus on large-scale neural systems
in humans.
Kathleen E. Cullen is a professor of biomedical engineering at the Johns
Hopkins University School of Medicine. She holds joint appointments
in neuroscience and in otolaryngology. Her expertise in systems andcomputational neuroscience uses state-of-the-art methodologies thatmanipulate and monitor neural circuits to understand the neuralcomputations that predict the consequences of natural self-motion andthe implications for perception and action.
Simon B. Eickhoff is a neuroimaging researcher at the Heinrich-Heine
University Düsseldorf and the Forschungszentrum Jülich. At the interface
between neuroanatomy, data science and medicine, his work combines
brain mapping, modelling of inter-individual differences and brain–
phenotype relationships as well as machine learning for single subject
prediction of behavioural traits and precision medicine.

role of astrocytes in neural circuits. The Khakh laboratory develops
and uses novel genetic and optical methods to explore glial biology
in vivo.
Jürgen A. Knoblich is Director of the Institute of Molecular Biotechnologyof the Austrian Academy of Sciences in Vienna. He is a developmental
neuroscientist studying human brain development and psychiatricdisorders. Researchers in his group have developed a method for growing
human brain tissue in the laboratory. They can recapitulate humanembryonic brain development during the first trimester and analyse thedevelopmental defects leading to neurological disorders.
Panayiota Poirazi is a Research Director at the Foundation for Research
and Technology-Hellas (FORTH) in Heraklion, Crete, Greece. She uses
mathematical and computational techniques to develop experimentally
constrained models of single neurons and neuronal networks, aiming at
inferring the contributions of dendritic computations to complex brain
functions.

Martha J. Farah is a cognitive neuroscientist at the University ofPennsylvania, where she directs the Center for Neuroscience & Society.
Her current research is focused on the relations between socio-economic
status and the brain.

Russell A. Poldrack is the Albert Ray Lang Professor in the Department of
Psychology and Professor (by courtesy) of Computer Science at Stanford
University, and Director of the Stanford Center for Reproducible
Neuroscience. His research uses neuroimaging to understand the brain
systems underlying decision-making and executive function. His laboratory
also develops neuroinformatics tools to help improve the reproducibility
and transparency of neuroscience.

Yukiko Goda is Deputy Director and a team leader at the RIKEN Center
for Brain Science. Her research addresses the cellular principles by which
synaptic strengths are set and dynamically modified at individual
synapses in relationship to other synapses in defined neural circuits. Recent
efforts in the group also target roles for astrocytes in synapse regulation.

Marco Prinz is a neuropathologist at the University of Freiburg, Germany.
Currently, his research group aims to understand myeloid cell biology in
the CNS during health and disease and studies the impact of the CNS
endogenous immune system on the pathogenesis of neurologicaldisorders such as neurodegenerative and neuroinflammatory diseases.

Patrick Haggard leads the ‘Action and Body’ research group at theInstitute of Cognitive Neuroscience, University College London.

Pieter R. Roelfsema has been Director of the Netherlands Institute for
Neuroscience, Amsterdam, since 2007. He studies visual perception andperceptual organization, as well as plasticity rules. He aims to develop aneuroprosthesis for the visual cortex to restore a rudimentary form ofvision in blind individuals.

Hailan Hu is a professor at Zhejiang University School of Medicine, China.
Her research interest focuses on how emotional and social behaviours are
encoded and regulated in the brain, with a main focus on the neuralcircuitry underlying depression and social dominance.
Yasmin L. Hurd is Professor of Psychiatry, Neuroscience andPharmacological Sciences at the Icahn School of Medicine in New Yorkand Director of the Addiction Institute in the Mount Sinai Behavioural
Health System. Her research focuses on opioid abuse and thedevelopmental consequences of early drug exposure (particularly
cannabis) using multidisciplinary and translational approaches inhumans and animal models.
Sheena A. Josselyn is Senior Scientist at the Hospital for Sick Children(SickKids), a Professor at the University of Toronto in Canada and a
Senior Fellow in the Canadian Institute for Advanced Research (CIFAR).
She is interested in understanding how the brain uses information.
Her laboratory uses a variety of experimental techniques to probe
memory function in mice.
Baljit S. Khakh is a Professor of Physiology and Neurobiology at Universityof California Los Angeles. His expertise centres on glial biology and the

Nature ReviewS | NEURoSCiEnCE

Tara Spires-Jones is Professor of Neurodegeneration and a UK Dementia
Research Institute Group Leader at the University of Edinburgh. Her groupstudies the role of synapses in brain degeneration and resilience. Using
pioneering imaging techniques, her group has discovered links between
genetic risk factors for Alzheimer disease and synaptic degeneration.
Mriganka Sur is a neuroscientist at the Massachusetts Institute ofTechnology, where he directs the Simons Center for the Social Brain.
His laboratory studies mechanisms of brain wiring and processing,
especially in the cerebral cortex, and how they go awry in disordersof brain development.
Hiroki R. Ueda is a professor at the University of Tokyo and a team leader
in RIKEN BDR. He has an expertise in systems biology and focuses on
mammalian sleep–wake cycles. He invented the whole-brain andwhole-body clearing and imaging methods called CUBIC and thenext-generation mammalian genetics for one-step production andanalysis of knockout and knock-in mice without crossing.

volume 21 | October 2020 | 525

Viewpoint
high-density recording across brain regions
and the ability to target perturbations inneural circuits are providing new insight
into how specific brain regions, such as
the cerebellum, actually perform thisessential computation at the level of single
neurons. Second, recent advances in our
understanding of sensory processing
during natural behaviours have motivatedthe investigation of neural circuits inreal-world environments. For example,
the brain must recalibrate its encoding of
sensory information in conditions in which
relationships between motor signals andreafferent sensory feedback are altered. Yet
studies of the neural basis of our sense of
direction and place have traditionally usedvirtual reality set-ups that unintentionallygenerate persistent sensorimotor
mismatches compared with naturalnavigation and/or recorded only coarse
measurements of behaviour (for example,
head position). Ongoing experiments
focused on understanding circuit-levelneural coding strategies during navigation— using more precise and comprehensive
measurements of complex animal behaviour— are now beginning to elucidate the neuralcomputations underling navigation inchanging real-world environments.
Simon B. Eickhoff. Cognitive and systems
neuroscience has gone through a period of
explosive growth over the past two or three
decades following the advent, expansion
and maturation of in vivo neuroimaging.
Among the many noteworthy developments
over that period, the increase in sample size
certainly stands out. In the late 1990s, MRI
studies involving fewer than 10 individuals
were the norm. However, this number
has grown continuously and nowadays
studies with several dozen if not hundredsof participants are common. Moreover,in particular for task-free data, analysesof 10,000s of individuals have become
feasible due to increased data-sharing andlarge-scale efforts such as the UK Biobank.
Although these developments could berightfully attributed to increasing awarenessabout power and replicability, another main
driver is the evolution of study objectives.
Earlier studies often focused on robust
effects that are often well discernable evenin individuals, but paradigms became more
complex and differences between conditions
became increasingly subtle, highlighting
the aforementioned concerns. Moreover,although the search for the ‘neural correlatesof …’ in group analyses has long dominatedthe field, more recently, inter-individual
differences have come more into focus.
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In this context, one of the effects, andfurther driver, of large sample sizes is thegrowing focus on individual inference using
machine learning. The mass-univariate
investigation of within-sample associations
is being increasingly abandoned infavour of multivariate models that are
better suited for capturing the richnessof MRI data. The latter also allow theprediction of inter-individual differencesin phenotypical (for example, behavioural)characteristics in new, previously unseen
individuals. Machine-learning approaches
may thus hold the key towards deliveringon some of the promises that have beenabundant in the literature ever since thestart of neuroimaging. Throughout
the decades, many neuroimaging studies
were ultimately motivated by clinicalissues, which can hardly be realized bygroup-mean differences but may become
possible using multivariate, predictive
approaches. Even though further maturationand consolidation of the field is needed,
it has brought real-life applications ofneuroimaging research into reach.
Finally, although hardly a new
development but rather a controversy
going back 150 years to Broca and
Lichtheim, the continued change in focus
between localizing and connectionist
perspectives is intriguing. The former
became massively popular with the abilityof task-based functional MRI (fMRI) to
discern differences in activity between
conditions, whereas the latter surged withthe development of methods for studying
structural and functional connectivity
in vivo. More recently, novel methods
for in vivo brain cartography at the level ofgroups or individuals on the one hand, andincreasing focus on network-theory anddynamical system modelling on the other,have further advanced both perspectives.
What is missing, though, and hence
remains a key challenge for the future, areunified accounts for regional segregationand system-wide interactions in the brain.
How to define the building blocks of thehuman brain and determine their likely
context-dependent integration into larger
systems, their inter-individual variability andtheir relationship to behaviour continue tobe grand but crucial challenges.
Martha J. Farah. For most of the long
history of neuroscience, the field has been
defined by two main goals: to understandthe brain and to alleviate neurological andpsychiatric disorders. These two goals haverecently been joined by a third: to enhancediverse non-medical endeavours. New

subjects of study with the prefix ‘neuro’have proliferated, from neuroaesthetics toneurowarfare. This historical turn in the usesof neuroscience arrived right around theturn of the new century, so we are now about
20 years into the era of ‘neuroeverything’.
The idea of neuroeverything
understandably evokes eye rolls, withinneuroscience and without. Many of these
exuberant interdisciplinary connections will
not turn out to be useful, but I believe thatsome will. Why? Because neuroscience is —in principle — relevant to any field that seeks
to understand, predict or influence humanbehaviour. Whether and when it becomesrelevant in practice is an empirical issue,
but I see no reason to rule it out yet. Indeed,
there are glimmerings of real progress insome of these fields.
Neurolaw took, as its initial focus,
questions of criminal responsibility andpunishment. Neuroscience has helped shore
up the rationale for separate standards ofpunishment for juvenile offenders, given
their incomplete brain development, and has
been cited in US Supreme Court opinions
on the matter. Neurolaw now encompasses
issues beyond responsibility, reflecting the
law’s many intersections with psychology
and behaviour. For example, cases involving
claims of pain are common in the US legal
system, with hundreds of billions of dollars
in awards at stake each year. Pain is, ofcourse, invisible, and there are obvious
motivations for malingering. Brain imaging
research on pain has revealed potential
biomarkers, which may eventually prove
useful in such cases (indeed, they havealready been used, but this was undoubtedly
premature). Neuroeducation has evolved
beyond vague, in-principle statements
about brains and learning to specific new
insights about how children learn to readand do maths, and has delineated different
types of learning difficulties and ways toindividualize instruction based on thesetypes. Neuromarketing is based on theidea that people often cannot or will not
articulate their true preferences, whether
for product packaging or political speeches,
but that neural correlates of emotion andattention offer a new window on their
inclinations. The academic literature backs
up many of these claims. Companies aroundthe world use neuromarketing, and political
campaigns in a number of counties arereported to have consulted neuromarketers.
Has neuroscience revolutionized theway we teach, do business or pursue justice?
Of course not, but that is an unreasonably
high bar. Has it contributed to progress inthese previously unrelated fields? My answer
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Viewpointis a cautious, provisional ‘yes’. The long-term
impact of neuroscience on these and otherfields remains to be seen, but what else
would we expect after scarcely 20 years?
The history of neurology and psychiatry
shows that applying neuroscience tocomplex problems is slow and hard. Patienceand an open mind are necessary, if not
sufficient, for progress. So, for the time
being, let us resist rolling our eyes.
Yukiko Goda. Neuroscience researchover the past two decades has been
catapulted by technological advances
enabling manipulation and monitoringof neural activity in behaving mice atever-higher spatial and temporal resolution
over increasingly broad brain areas.
Such developments have dramaticallystimulated circuit-based analysis of theneural systems underlying cognitive
functions and behaviour. Omics approaches— transcriptomics, proteomics andconnectomics — are also providing novel
views into the molecular and cellulararchitecture of the brain, revealing an
extraordinary degree of complexity of
individual cell types and their subcellularcompartments across different brain areas
and brain states.
In synapse biology, the past two decades
have witnessed substantial insights into
the molecular underpinnings of synapse
organization and plasticity. We have learned,
for example, how synapse specificity isdriven by a combinatorial synapse adhesion
code, that a neuron can pack different
transmitters into single synaptic vesicles,
sometimes even both glutamate and GABA,
and that the principal mediators of synaptictransmission can dynamically move beyond
the boundaries of individual synapses. Local
protein translation, whose occurrence in
dendrites is now well recognized, may also
occur in presynaptic terminals. Collectively,
the findings underscore the broadplasticity repertoire possessed by synapses.
Dendritic spine plasticity during learning
has continued to attract attention over the
past two decades. The causal relationshipbetween learning and spine structuralchanges has helped establish the hypothesis
originally proposed by Donald Hebb
that synapses are the building blocks ofmemory.
Excitatory principal neurons typically
carry tens of thousands of synapses, eachcapable of expressing various forms ofplasticity. A key issue is to close the widening
gap between our circuit-level understandingof various neural systems, which is gaining
momentum, and our understanding of
Nature ReviewS | NEURoSCiEnCE

the behaviour of individual synapses
constituting the network. For example, how
might synapse-specific Hebbian plasticity
drive memory engrams that are represented
by cellular networks? Furthermore, given
our increasing understanding of Hebbian
plasticity mechanisms, how do neighbouring
synapses interact and balance distinct
forms of synaptic plasticity, in some cases
involving communication to the nucleus,
to impact dendritic computation? Also, in
contrast to spine plasticity, little is knownof the contribution of presynaptic plasticity
to particular circuit functions and related
behaviours.
Further cell biological insights into theworkings of synapses are also warranted.
For example, super-resolution microscopy
has provided a glimpse into the adhesive
organization of a synapse where presynaptic
release sites are aligned to nanoclusters of
postsynaptic scaffolds. How plastic are suchnano-columns and how are they regulated
to guide transmitter release? Moreover,besides the presynaptic and the postsynaptic
mechanisms, a better understandingof the contribution of glial signalling andthe extracellular matrix that occupy theinterstitial space would be crucial, as are
the roles of neuropeptidergic signalling.
Finally, the vast number of synapses in thebrain are highly heterogeneous, even in
the same neuron. Deciphering how synapse
diversity arises in the context of specificcircuit functions might shed new insights
into synaptic information processing in
the brain and may help in tackling diseases
linked to synapse dysfunction, of which
there are many and with mechanisms yet
to be clarified.
Patrick Haggard. Since classical times,
scientists have noted how human actions
on the one hand seem to have a degree of
unpredictability, and on the other handare accompanied, at least sometimes,
by a subjective feeling of being open,or ‘up to me’. These observations often
lead to theories of human action being
somehow exceptional, or escaping fromnormal mechanistic causation. Advances intheory and methods over the past 20 years
have shown that several key aspects ofhuman voluntariness can be explained
neuroscientifically, pushing back against
exceptionalist theories.
Important progress has come fromstudies on decision-making. The
introduction of formal decision theory
into perceptual neuroscience, notably
through drift diffusion models, turned
out to be surprisingly informative inunderstanding internally generated, as well
as stimulus-guided, actions. For example,
when stimulus evidence is ambiguous,internal motor noise can still drive adecision variable over the action threshold.
A causal role of biological noise in voluntaryaction remains difficult to reconcile with
our subjective feelings of deciding andcontrolling. However, being clear about therole of noise can at least help to distinguishthe putative signals — internal drives, goals
and dispositions — that underlie voluntaryaction.
A second area of progress comes fromreinforcement learning (RL). Action choices
are often based on learning from experience
what worked previously in similar
contexts. The past two decades have seen
increasing focus on RL in human action.
‘Model-based’ RL can be viewed as an
explicit account of how thinking translates
into acting — perhaps the fundamental
challenge of voluntary action. Exploratory
or exploitative choices in action–outcome
learning may correspond to the innovative
or creative aspect of human voluntaryaction, and to more routine and habitual
aspects, respectively. The significance of RL
lies in emphasizing that humans learn tobe voluntary. The movements of newborn
infants seem purposeless, yet healthy
adults learn to control highly complex
movements, so as achieve desired outcomes.
Interestingly, childhood amnesia means that
we generally cannot remember the periodof our lives where we lacked voluntaryagency. Recent learning studies suggest thatexperienced linkage between action andoutcome is boosted immediately after anerror, suggesting an important connection
between sense of agency and persistence intrying.
Voluntary action lies at the nexus of thestimulus–response chain and the action–
outcome chain. The past two decades haveseen substantial progress in both areas, often
driven by the combination of computationalmodels and mechanistic studies withanimals. The space that volition must occupy
is therefore more clearly defined. However,
we still know little about how people selectand maintain their goals. For this reason,
recent neuroscientific developments havenot undermined concepts of individual
psychological autonomy, but rather
described how such autonomy may arise,
how it is acquired and how it is deployed.
The tension between what neuroscienceteaches us about our volition and what weare taught by cultural traditions and by ourown subjective experience remains to beresolved.
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Viewpoint
Hailan Hu. One area I have seen booming is
psychiatric neuroscience, the research into
psychiatric disorders. Unlike neurologicaldisorders, which are often associated withdiscrete anatomical lesions, psychiatricdisorders used to be thought of as diseasesof unknown aetiology. Indebted to many
new mechanistic discoveries, the paradigm
is shifting and the boundary between
neurology and psychiatry is becoming
less distinct. This new paradigm is led
by revolutionary tools for brain imaging
and manipulations, which permit direct
testing, with unprecedented temporal andspatial precision, of the causal relationshipbetween the activity of specific neuralcircuits and behavioural or disease states.
In particular, aided by optogenetics orchemeogenetics, researchers can now mimicor mitigate aspects of different psychiatricstates, including obsessive–compulsive
disorder, addiction and depression, inanimal models, by activating or inhibiting
relevant neural circuits. By targeting theseneural circuits, researchers can rigorously
tackle the aetiology of psychiatric disorders,characterizing neural activity patterns
and pinpointing molecular and cellularabnormalities in relevant brain areas.
Another interesting and exciting
development in recent decades is the riseof social neuroscience, which explores
the neurobiological mechanisms of social
behaviour. As social behaviour involves
dynamic interactions between more than
one animal or human, there are inevitably
more variables that affect behavioural
outputs. Owing to this complexity, studies inthis domain used to be conducted more at
the correlative level or focused on hormonalor genetic traits. Now, ecologically relevant,well-controlled behavioural paradigms
in laboratory settings are being created,and new technologies for behaviouralmonitoring, recognition and manipulation
are being implemented. These advances
will enable more direct causality studies atmultiple levels, including the molecular,
cellular and neural circuitry levels. Most
excitingly, some studies have pioneered
tracking behavioural patterns, physiologicalparameters or even neural activities ofmultiple animals simultaneously. Suchadvancements will be particularly insightful
for studying social interactions in complex
and naturalistic environments.
In my view, one interesting, big-picture
question in psychiatric neuroscience is
how do some drugs rapidly ameliorate
or induce certain psychiatric symptoms?
Studies related to this question should not
be considered as standard pharmacological528 | October 2020 | volume 21

research on mechanisms of action, but‘straight roads’ towards the core brain
mechanisms of psychiatric disorders. Why
so? Psychiatric disorders are well known for
their slow progression, complex symptoms
and multiplex genetic and epigenetic factors,
making it a daunting task to understandthe underlying aetiologies. However, some
rapid-acting drugs quickly alleviate or
create a mentally disordered state, providing
a convenient and clinically relevant
perturbation tool to unlock the hidden
doors of psychiatric diseases. For example,
as antagonists of the NMDA receptor,
phencyclidine can rapidly induce symptomsof schizophrenia whereas ketamine canrapidly alleviate symptoms of depression.
Their actions imply that NMDA receptors
are key players and strong target candidates
for these disorders. This knowledge,
combined with brain-circuit-specificinformation acquired through localized
drug application and circuit manipulation,
should shed light on the diagnosis,
prevention and treatment of mental healthdisorders.
In terms of lines of research, I am
personally most excited about how social
dominance shapes different behaviouraltraits, and what special pharmaceuticalfeatures enable ketamine’s rapid andsustained antidepressant effects.
Yasmin L. Hurd. Each decade has normallybrought revolutionary advances to theneuroscience field, and the past two decades
have been exceptional. A few advances that
instantly resonate with me include thosethat have continued to address a major
challenge in neuroscience, that being
understanding the heterogeneous mosaicof brain cells. Recent single-cell sequencing
strategies and genetic tools have revealed vastdifferences in the molecular profiles of cells,
indicating the existence of potentially overa hundred different mammalian neuronaland non-neuronal cell types. Moreover,major technological advances combined
information about the genetic phenotype
of cells with optics, such as optogenetics,
and enabled fine-tuned control of neuronal
activity to elucidate the functional role ofindividual cells. From such advances, wehave gained better insights into the role ofdistinct cells localized within discrete neuralcircuits to directly modulate behaviours
relevant to, for example, decision-making,reward, fear and negative affect. Large
steps have also been taken in acquiringknowledge about the transcriptome andepigenetic landscape of the human brain.
This knowledge, taken together with datafrom the Human Connectome Project,which is mapping structural and functional
neural connections, will significantly impact
our understanding of neuropsychiatricdisorders. One other notable discovery isthat classic monoamine neurotransmitters,such as serotonin and dopamine, candirectly attach to chromatin, moving thefield to consider these neurotransmitters
not only as cell-to-cell receptor mediatorsof intracellular signalling but also as direct
regulators of gene transcription.
Several of the interesting big-picture
questions relate to understanding thelong-term consequences of early-life
events for neuropsychiatric vulnerability.
Thus, important questions relate to how
we conduct comprehensive longitudinalassessments (neuroimaging, biospecimens
and behavioural) of individuals — from fetal
development, childhood and adolescence— and how we identify early markers of risk
including epigenetic mechanisms. Some of
the big-picture questions directly impact
governmental policies and society, suchas what are the potential adverse neural
effects of cannabis and cannabinoids on
mental health, and what are their potential
therapeutic properties? Moreover, the recent
opioid epidemic has also raised big questionsof how to develop novel treatments forchronic pain conditions that are devoid of
addictive properties.
In terms of lines of research, I am
particularly excited about the growing useof machine-learning strategies that leverage
neural-derived omics and other complex big
data sets to improve prediction models about
neurobiological substrates linked to specificphenotypes. The potential for enhanced
precision treatment of neuropsychiatricdisorders is also particularly exciting withthe continued development of optogenetic,
DREADSS, CRISPR and nanotechnology
tools, which have the potential to be
applied in humans, to target cell-specificand circuit-specific neural processes.
In addition, the new research on histone
monoaminylation is exciting as it opens
up new epigenetic processes to target
therapeutically. Last, I continue to be excitedabout efforts to expand molecular, structuraland functional knowledge about the humanbrain. Such knowledge is essential to
improve the translation of animal studies
and to improve treatment strategies. Overall,
my excitement stems from research efforts
that provide in-depth neurobiologicalknowledge about neuropsychiatric disordersthat is directly relevant to the humancondition and that can provide criticallyneeded new therapeutic interventions.
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Viewpoint
Sheena A. Josselyn. Understanding how
we learn and remember has fascinatedphilosophers and scientists for hundreds ofyears. In the past 20 years, there has beena seismic shift in how we (the field) study
memory. This methodological tremor
was triggered by our newfound ability togain a firm experimental grip on the basic
cellular unit of memory storage, the engram(or memory trace). The general idea thata specific memory is stored as enduringchanges in the brain is centuries old, but it
was not until the beginning of the twentiethcentury that Richard Semon introduced
the term ‘engram’ to describe the neuralsubstrate for storing and recalling memories.
An experience, Semon essentially proposed,
activates a small subset of cells that undergo
persistent chemical and/or physical changes
to become an engram. Memory retrieval
occurs when sensory cues later reactivate
this engram. Although the idea of an engram
may have been theoretically appealing,similar to hunting a mythical creature, assoon as scientists tried to experimentally
grab an engram, it seemed to slip away.
Over the past 20 years, though, thedevelopment of an ever-increasing array of
new tools allowed scientist not only to find but
also to manipulate ‘engram cells’ across several
model species. The ability to catch an engram
proved to be an absolute game-changer. For
instance, previously we attempted to examine
the biochemical and electrophysiologicalsubstrates of memory by grinding up an
entire brain region or ‘listening’ to theelectrophysiological changes of a broad
population of neurons (as in field long-term
potentiation recordings) after a learning event.
With engram technology, changes in engram
cells alone could be studied, free from thepotentially obscuring effects in non-engram
cells. The mnemonic wheat could finally beseparated from the chaff.
With engram technology, we can now
implant ‘artificial memories’ in mice.
Indeed, a handful of laboratories’ recent
studies showed it is possible to create
entirely artificial percepts or memories in
mice, without any external stimuli, simply
by manipulating cell ensembles. Thesescience-fiction-type findings verify that weare on the right track. In addition, studying
memory at the level of engrams is leading
to novel insights into the very fundamentalbuilding blocks of memory. For instance,
we are beginning to understand how cells(initially excitatory neurons, and now
expanding to include other cell types)
are allocated to an engram, how thearchitecture of an engram impacts memory
retrieval (for instance, whether events are
Nature ReviewS | NEURoSCiEnCE

remembered distinctly or organized into
boarder conceptual knowledge), how thepassage of time or new information changes
engrams, the extent to which engrams are
static or dynamic and how information
becomes momentarily inaccessible (as in
the tip-of-the-tongue phenomenon) or
irretrievably forgotten (more permanent
amnesia).
That being said, many many questions
remain. For instance, exactly how (andwhere) is information stored in the brain?
Is it stored in cell ensembles, synapses,
molecules or DNA? And, more basically,
what do we even mean by ‘information’?
The answer to these ‘hard’ memory
questions will likely require continued
crosstalk between researchers examining
memory in humans, rodents, fish, flies andeven in silico.
Baljit S. Khakh. Twenty years ago,
neuroscience consisted largely of a
cottage industry of researchers pursuing
open-ended questions for which they
had amassed a great deal of specialized
knowledge and skills. Since then, we havewitnessed the dawn of ‘industrial scale
neuroscience’, pursued as large organizedinitiatives, often in private institutes. Thesetypes of projects seek to tackle big problems
in defined periods. They are having an
inexorable and substantial impact in thedetails of the science itself and, increasingly,
in how others plan and do their own science.
Pundits, academic institutions and funding
agencies are also pondering how they canalign faculty to attack key problems at a scalethat is not possible in single research groups.
The promise of such approaches is that
progress could be rapid and efficient, but
the potential risk is that laboratory research,
unless thoughtfully shepherded, may lose
some of the creativity that has and continues
to drive discovery and innovation in small,hands-on research groups. Undoubtedly,both models deliver in distinct ways.
In my own field of glial biology,
the availability of new genetic, opticaland instrumentation tools has allowed
neuroscience to be explored in ways thatwere previously not possible, and thishas changed the field to the extent thateven cellular–molecular projects can now,for example, evaluate the behaviouralimplications of mechanistic findings.
Another development has been the growthof big data — including genomic, geneand protein expression, electrophysiology,
imaging and behavioural data — and thenecessity to process and analyse them withcomputational methods.

In relation to neurons, glia are still anunderstudied cell population. I think themost interesting questions being asked in
my area relate to how glia regulate neuralcircuits, how glia contribute to behaviourand how glia contribute to the function of
the brain as a highly evolved multicellularorgan and not just as a collection of neuronal
circuits. Including glia into how complex
functions of the brain are performed also
holds the promise of shedding new light on
disease mechanisms as well as brain–bodyand neuroimmune interactions.
I am particularly excited about theprospect of manipulating and targeting glia
as endogenous neuromodulators to produce
disease-related phenotypic benefits incomplex brain disorders. This is a worthwhile
direction to follow: pursuing neuronalmechanisms alone over the past 20 years has
not been as fruitful as was once anticipatedin terms of disease-modifying treatments.
Additional parallel and orthogonal
approaches are now also necessary.
At a broader level, I think mechanistic
studies and computational neuroscience
are going to be critical to make senseof the vast amount of precisely tracked,
technique-driven, but essentially descriptive
behavioural data that are emerging in thefield as a whole.
Jürgen A. Knoblich. Like other fields,
neuroscience was profoundly influenced
by the arrival of the post-genome era. The
ability to compile complete inventories
of receptors, channels and other elements ofneuronal information processing, together
with emerging methods for measuring
their expression and function across cell
types in a comprehensive manner, havebrought neuroscience to another level.
A second groundbreaking development
was the establishment of optogenetics. The
ability to turn neurons on or off has mergedfunctional neuroscience with cell biology.
Mapping brain circuits and connecting themto specific behaviours has become possible andwill fill the gap between neuroanatomy
and the functional analysis of individual
neurons and their electrical properties.
In neurodevelopment, my own fieldof research, the past two decades saw theidentification of radial glia cells and theirvarious descendants as the progenitors for
all neurons in the cortex. Importantly, somesubtypes of these progenitor cells are uniqueto primates and this has triggered a new era
in which analysing unique features of thehuman brain is within reach.
I am convinced that the next decade inneuroscience will be the era of the humanvolume 21 | October 2020 | 529

Viewpoint
brain. It is increasingly clear that humanbrains are not just enlarged versions ofanimal brains. They have unique featuresand develop in a unique way, and we areat the verge of understanding why that is.
We have identified human-specific cell
types and are beginning to understandtheir role in making our brains so powerful.
Genome-editing, functional genomics andorganoid models can be combined to allow
genetic analysis in human brain tissue
models.
Eventually, those tools will endow us withthe ability to ask fundamentally importantquestions for understanding ourselves:
what makes us human and what genetic
changes are responsible for the enormous
complexity and cognitive power of thehuman brain? What makes us individual?
Why does my son think like me and whereis this encoded in the genome? And why dohumans with alterations in specific genes
think differently and develop symptoms
we call autism or schizophrenia? Besides
providing fundamental biological insights,
the answers to those questions will also
profoundly change the medical treatmentof neurological and psychiatric disorders.
Needless to say, I am particularly excitedabout organoids. It may be surprising tosome that I do not think that they will
replace animal experiments or that they
are or will ever become a comprehensivemodel for the human brain — but they
allow us to take the last step towards asking
specific questions about our own brain.
Currently in their infancy, I am confident
that organoids will develop into models
for investigating certain neurobiologicalprocesses. Establishment of organoid models
for functional brain circuits and for major
long-range connections within the brain
are within reach and can be combined with
optogenetics to probe human-specific brain
features at the circuit level. I am also excitedabout the power of combining in vitro
models with ever-improving fMRI brain
imaging methods to understand humanbrain circuits.
Panayiota Poirazi. The turn of thetwenty-first century was marked byremarkable discoveries regarding thecellular and subcellular properties ofneurons. As a dendrites’ aficionado,
I was thrilled by the unveiling of their richconductance repertoire, and their ability tocompartmentalize electrical, chemical andmolecular signals and support local spiking.
Empowered by computational modelling,these discoveries suggested that dendrites
should no longer be viewed as passive cables
530 | October 2020 | volume 21

but as powerful computational units that
greatly expand the processing and storage
capacity of individual neurons.
Today, the technological advances ofthe past decades allow the testing of these
propositions through causal probing ofmultiscale processes that operate in thebrains of behaving animals. New tools
allow the tracking of synaptic activity andplasticity in vivo (for example, SynTagMA
and GluSnFR), the characterization of
inputs to individual neurons (for example,
via rabies viruses) and the selective
manipulation of activity (optogenetics),
often restricted to specific compartments
located far from the surface (for example,
via prisms), while recording from hundredsof neurons across different brain areas.
Voltage indicators are also coming ofage and will soon replace slower molecules,
and computational modelling, driven by
this slew of multimodal data, has advanced
to account for dendritic processing at thesingle neuron and circuit levels. These
new tools enable unprecedented accessto the microscopic, mesoscopic andmacroscopic levels of computation andpromise exciting new discoveries. The
past decade has also witnessed a closer
and more fruitful collaboration between
theorists and experimentalists. Numerous
dendritic contributions, many of which wereoriginally predicted by models, have been
now confirmed in awake behaving animals
for critical brain functions such as learningand memory, sensation, navigation and evenperception and anaesthesia.
Yet most of our current understanding islimited to dendritic effects on single-neuron
processing or circuit activity. Establishingcausal links between dendritic function
and behaviour is the ultimate challenge and,to me, the most exciting direction forthe years to come. A synergistic, online
approach will be critical for achieving
this goal. For example, monitoring ofsynaptic, dendritic and neuronal activity
from different cell types and brain areas
during various behaviours could be used toconstrain large-scale network models in acontinuous, online manner. The models will
predict how changes in dendritic properties
may alter aspects of behaviour and online
tests will refute or verify predictions, thus
closing the loop and updating both models
and hypotheses. I am also fascinated by thequest for understanding dendritic functions
across different cell types and species.
Exciting new discoveries suggest thatinterneuronal dendrites can also integrate
incoming signals in non-linear ways and thathuman dendrites can implement advanced

computations previously attributed to
networks. Finally, I believe that adopting
dendritic properties in artificial intelligence(AI) holds great promise. Incorporationof dendritic processing in both software(for example, deep neural networks) andhardware (for example, CMOS devices)
technologies is underway, hoping to achieve
brain-like performance in complex tasks
such as reasoning and one-shot learning.
These efforts are expected to open an
entirely new era in bio-inspired machinelearning and AI in the following years.
Russell A. Poldrack. In my view,
one notable development in cognitive
neuroscience over the past two decades
has been the advent of computational
theories that are being used to bothdrive experimentation and interpret
neuroimaging data. This has occurred
across many subfields, most notably
in visual object recognition, but also incontent areas as diverse as decision-making,memory and motor control. Together
with new methods for modelling and theanalysis of neuroimaging data, these models
have provided a tool to more clearly linkfunctional imaging signals to underlying
computational mechanisms.
Another important development
has been a transition from the highly
localizationist approaches of earlyneuroimaging research to a focus on thehierarchical structure and dynamics of brain
networks. Much of this focus has arisen
from task-free fMRI studies, which havedemonstrated structured correlations withclear functional relevance at multiple levelsof spatial organization. In addition, we haveseen the advent of ‘network neuroscience’,leveraging the tools of network science tomathematically characterize brain networks.
It might once have been legitimate to referto neuroimaging as ‘blobology’, but in mostareas of neuroimaging research those days
are largely gone.
For me, one of the most interestingquestions is how we can reconcile theviews of brain function that are currently
inherent in the computational neuroscienceand network neuroscience approaches.
Network neuroscience has provided
important insights into the structureand dynamics of brain networks, but themethods used for these analyses generally
treat ‘nodes’ as fungible beyond their specificlocation within the network. By contrast,computational neuroscience has provided
insights into the specific computational
functions that are performed by individual
brain circuits, but has had relatively
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Viewpoint
little to say about the complex dynamics
that occur when many such circuits areinterconnected at the scale of an entire brain.
Bridging these two approaches will requirea rapprochement between very different
analytical approaches coming from physics
and machine learning, but it will be essential
to developing a fuller understanding of how
the brain gives rise to high-level cognitive
functions.
I am particularly excited about recent
work that has begun to define the functionalarchitecture of individual human brains
with much higher precision than before.
After my initial work in the MyConnectome
study, a number of laboratories have begun
using a ‘deep phenotyping’ approach inwhich individuals are scanned repeatedly
and characterized individually. Thisapproach has already provided new insights
into the fine-grained organization of theentire human brain, highlighting boththe universality and diversity of functionalorganization at different levels, as well as
identifying new functional features that were
not visible in earlier group-averaged fMRI
studies. Going forward, I am particularly
excited to see these approaches taken into
the study of mental illness. Given the great
deal of variability in the time course ofmental illness, understanding the dynamics
of brain function in relation to the
dynamics of disease is crucial to enablemechanistic insights that can lead to newtreatments.
Marco Prinz. In the past two decades, wehave been eyewitnesses to a renaissanceof neuroimmunology research withinneuroscience. In particular, a special focus
has been made on understanding thefunction and heterogeneity of CNS myeloid
cells including parenchymal macrophages(that is, microglia) and macrophages
at CNS interfaces, such as perivascular,
meningeal and choroid plexus macrophages.
Conversely, when I started my researchon microglia in the late 1990s at the Max
Delbrück Center for Molecular Medicine in
Berlin, neuroscience was almost exclusively
neuron-centred and glial researchers wereconsidered as rather exotic foreigners,
neither belonging to neuroscientists nor to
immunologists.
The big bang of microglia research
was certainly the development of the
CX3CR1GFP mouse line by Steffen Jungin 2000 when he was a postdoc in Dan R.
Littman’s laboratory in New York. This
pioneering work allowed for the firsttime the visualization of microglia in vivo
and paved the way for several important
Nature ReviewS | NEURoSCiEnCE

microglia discoveries in the following years.
For example, the unexpected finding that
‘resting’ microglia are continuously activein the living mouse brain shed new light on
microglial functions in the normal brain
and made clear that these cells are highly
active guardians of brain homeostasis.
Along this line, microglial cells were
subsequently identified as key CNS intrinsicarchitects that shape neuronal circuits
during development and homeostasis bya process called synaptic pruning that waslater similarly found to be present duringpathological processes, such as Alzheimer
disease.
The most burning questions inneuroimmunology dealing with the innate
immune arm are how microglia contributeto CNS diseases and how this contribution
can be modulated. Big international
genome-wide association studies in recent
years have identified an ever-growing
number of genetic risk factors associatedwith several disorders — such as multiple
sclerosis, Alzheimer disease, Parkinson
disease and others — that are expressedby CNS myeloid cells, like microglia. The
major challenge now is to understandhow intergenic variations of several locijointly lead to changes in transcription
or translation and, ultimately, contributeto disease. Hopefully, rare monogenetic
disorders primarily and solely affecting local
tissue macrophages (for example, disorders
caused by CSF1R mutations) will help todecipher the underlying mechanisms ofnormal and disturbed immune regulationin the CNS and might finally facilitate thefuture development of myeloid-focused
therapeutic approaches.
There are several exciting novel
technological developments that currently
electrify neuroimmunologists around theglobe. A new era of microglial research has
just begun. The urgent need to understandmicroglial biology led, for example, to thedevelopment of microglia-like cells fromembryonic stem cells or induced pluripotent
stem cells. Further thrilling techniques in thefield include brain organoids, assembloids
and xenotransplantations that will hopefully
permit the study of environmental andgenetic factors in microglia-mediated
CNS diseases. In the future, multi-omics
approaches to profile genetic, epigenetic,
proteomic and metabolomic cellularstates at the single-cell level, novel animalmodels, imaging and fate-mapping tools,
patient-derived microglial cells and other
innovative techniques will help us todecipher more microglial secrets around
CNS development, homeostasis and disease.

Pieter R. Roelfsema. Advances intechnology have driven progress in systems
neuroscience in important ways overthe past 20 years. New methods became
available to measure and control the activityof many neurons with high precision.
Another noteworthy development is
the increasing influence of AI, allowing
neuroscientists to better understand synaptic
plasticity rules and information processingin the brain.
A major change in visual neurosciencehas been the focus on the mouse as a model
to study vision. Many researchers would nothave predicted this in 2000, because mice
do not have a particularly well-developed
visual system. The disadvantage ofstudying a species with poor visual acuity
is offset by the many powerful tools now
available for mice, including optogenetics,
two-photon imaging, tracing techniques
and interventions using viruses andtransgenic mouse lines. Even good-old
electrophysiology has been improving.
Researchers used to make recordings fromone cell at a time, but new probes permit
the study of many hundreds of neurons
simultaneously. Hence, recent papers
report the activity of tens of thousands ofneurons, distributed across many corticaland subcortical regions during a single
behavioural task. For the development of
the field, it will be important to make thesenovel techniques available to use in species,
including non-human primates, with brainsthat more closely resemble human brains.
An important trend has been themove towards open science and open data,
providing researchers access to large
data sets. This leads to a more efficientuse of resources and will increase thereproducibility of results by making it easierto evaluate the validity of the conclusions ofan article. By itself, access to large amountsof data is not enough to understand thebrain, however. Fortunately, neuroscientificinsight has started to benefit from theprogress in AI. In vision, artificial neuralnetworks are able to transform raw sensory
input into object categories; that is, they
extract meaning. Importantly, the tuningof units of these artificial neural networks
resembles the tuning of neurons in thebrain, helping neuroscientists to understandhow the many areas of the brain’s own‘deep networks’ contribute to the step-wisetransformation of pixels into semantic
codes. Furthermore, AI is now also inspiring
insights into how connections in deep brain
networks can be trained efficiently.
The enhanced understanding andaccessibility of neural codes through AI also
volume 21 | October 2020 | 531

Viewpoint
opens up new possibilities for interfacingwith the brain. A recent study used AI to
decode language from brain activity ofhumans. This methodology can be applied in
the future to help people with severe formsof paralysis in their communication withthe outside world. In combination with newhigh channel count interfaces, researchers
may soon read and write to the brain with
unprecedented precision, opening new
avenues for therapeutic intervention. Indeed,approaches are underway to create new
prostheses for blind individuals by directly
writing information captured by a camera to
the visual cortex, skipping the malfunctioning
eyes. As soon as these high-density brain
reading and writing are applied in humans,
there are likely to cause a new wave of
scientific insights into the neuronal processes
underlying the human mind.
Tara L. Spires-Jones. Exciting new lines
of investigation have emerged over the
past two decades in the field of Alzheimer
disease research, including understanding
how pathological proteins damage the brain,
why certain neurons and brain regions arevulnerable to degeneration, bridging the
gap between genetic and epidemiologic risk
factors and the brain changes that cause
dementia, and understanding how glia
contribute to neurodegeneration.
The field of dementia research has been
dramatically advanced by technology overthis period. For example, although thepathological lesions that define Alzheimer
disease were described in the early 1900s
and the ability to study pathology withhistology was well established over 30 years
ago, the past 20 years has seen a boom in
the ability to image these pathologies
in living people over time with positronemission tomography, to study thepathological proteins in exquisite detail withcryo-electron microscopy and to examine
the toxic oligomeric forms of these proteins
within individual synapses in animal models
and human tissue. GWAS starting in theearly 2000s highlighted new pathways
involved in disease pathogenesis, which arebeing followed up using new technologies
like single-cell transcriptomics and CRISPR
gene editing.
Some of the biggest changes in thefield over this time frame have not been
neuroscience-specific. The explosion of theability to share data has been of real benefit toneuroscientists, and this is likely to become
even more important over the next 20 years.
We have also seen major changes in
the way we conduct neuroscience. Thereis an emerging awareness that early-career532 | October 2020 | volume 21

researchers face many difficulties inpursuing an academic career. Althoughmuch more work is needed, groups suchas the FENS-Kavli Network of Excellence
are striving to support people coming up in
the field and to enhance the diversity andinclusivity of neuroscience. In my view, we
also collaborate more in neuroscience than
we did 20 years ago, with collaborations acrossdisciplines and industry, and through large
strategic initiatives like the UK Dementia
Research Institute, the US Brain Initiative andthe EU Human Brain Project. The impending
global health crisis that dementia poses andthe current pandemic also highlight thecontinued need for scientists to engage withthe public and policy-makers to help develop
evidence-based policies to meet societal
challenges.
For the next 20 years, I am excited abouttranslation. Perhaps I am a hopeless optimist
in the face of decades of clinical trial failures
for Alzheimer disease, but I think that weare close to life-changing treatments. It isextremely gratifying and humbling that
our work increasing understanding of
the fundamental brain changes that cause
dementia symptoms is contributing, albeit
in small part, to the development of clinicalapproaches and trials. I am also excitedabout the advancing understanding of thefundamentals of the brain and what makes us
human, our ability to think, learn, remember.
I agree with the European Brain Council’s‘Brain Mission’, which places understanding
the brain and treating brain disorders at theheart of a twenty-first-century mission
analogous to the twentieth-century missions
to put humans on the Moon.
Mriganka Sur. Two complementary fields
have witnessed notable developments overthe past two decades: understanding cortical
dynamics at the level of brain regions andneuronal populations, and cortical plasticity at
the level of synapses and clusters of synapses.
Recording the activity of large numbersof neurons, via multiphoton imaging andhigh-density probes, has given us novel
views of brain-wide activity, at single-neuron
resolution, in at least small brains.
Manipulating regional and cell-specificactivity with optogenetics has revealed
specific contributions of brain regions andcell classes to information processing.
Applying these tools to mice performing
behavioural tasks, we have derived acomputational understanding, building on
previous studies in non-human primates, ofregional circuit contributions to decisions,short-term memory and actions. At thesame time, recent findings puzzlingly

suggest the widespread expression of signals,
especially those related to action and reward,
throughout the brain. These studies point to
the importance of disentangling correlative
versus causal signals, and the roles of specificyet dynamic local and long-range circuits inbehaviour.
High-resolution imaging of synapses
and synaptic molecules has led to a richer
conceptual understanding of corticalplasticity. At excitatory synapses, plasticity
is largely implemented by Hebbian learning
rules, whereby coactive synapses arestrengthened; yet unregulated Hebbianstrengthening leads to saturated synapses
and unstable cell assemblies. Thus, synaptic
renormalization is necessary to stably encode
information in neurons and networks.
A key advance is the realization thatneuronal plasticity is implemented
by multiple cooperative mechanisms:
Hebbian changes at specific synapses arecomplemented by locally coordinatedplasticity at adjacent synapses that
renormalizes synaptic weights over dendritic
stretches, and by longer-term synaptic
scaling which further renormalizes weights
over the entire postsynaptic neuron.
These ideas, derived from measurements
in cortical neurons, likely apply to plasticity
of most neurons in the brain during
development and learned behaviours in
adulthood.
Plasticity is integrally related to cortical
dynamics and computations. A host ofmolecular and cellular processes are engagedby activity to modify cortical synapses
and circuits. For example, the inductionof plasticity in the visual cortex requires a
critical level of inhibition. Plasticity is gatedby modulators of attention and arousal,
such as acetylcholine and norepinephrine,
which regulate inhibitory–disinhibitory
circuits as well as astrocyte–neuron
interactions. The implementation ofplasticity requires signalling molecules thatmediate cooperative synaptic potentiationand depression, and molecules that regulate
synaptic scaling. How might particularmechanisms relate to specific circuits andtasks?
Understanding cortical plasticity anddynamics has two crucial applications.
First, it provides the basis for treating brain
disorders. Indeed, we are increasingly seeing
imaginative proposals for treating disordersof brain development and ageing thatderive, importantly, from basic mechanismsof plasticity and dynamics. Second, it canhelp build efficient computing machines.
Unlike computers with von Neumannarchitecture where processors and memorywww.nature.com/nrn

Viewpoint
are separate, the confluence of plasticity andprocessing in the same circuit elements is
a core component of brain computations.
Cortical plasticity almost certainly mediates
our robust perceptual schemas driven by
few examples, in contrast to large example
set-driven, yet brittle, machine perceptionthat is the current state of the art.
Hiroki R. Ueda. Just 20 years ago, we
successfully obtained a catalogue ofmolecules, in the form of a working draft ofthe human genome. Now, we are acquiring
catalogues of cells in entire brains and evenentire bodies in some mammals. One of thegreatest contributions to these achievements
has come from the remarkable development
and seamless integration of tissue-clearing
methods and light-sheet microscopy.
Tissue-clearing methods have become
increasingly high-performance, faster, safer
and easier to use. Light-sheet microscopy has
become more precise, with higher resolution,
faster and more isotropic. As a result, it is
now possible to perform whole-body or brain
profiling of cells and circuits in mice andother mammals. In fact, a three-dimensional,
single-cell atlas of the adult mouse brain
is already available. In addition to theacquired positional information of cells,
tissue-staining methods and image
informatics are also rapidly being developed
to characterize various cells in the brain andbody. In the near future, we may expect acolourful single-cell atlas of adult mouse, rat
and marmoset brains and bodies. Technicallyspeaking, the completion of even the humanwhole-brain atlas may not be a dream.
What comes after the catalogues of cells?
The catalogue of molecules was followedby the emergence of a new biology field
called systems biology. Systems biology atcellular to organismal levels has been hard
to realize until recently, because it firstrequires the identification of the cellular
networks where a catalogue of cells plays afundamental role. Now that the cataloguesof cells are being built and whole-brain cell
profiling technology is available, we may
be able to identify these cellular networks,
if the aforementioned approaches are
combined with perturbational technology,
such as next-generation genetics (genetics
without mating) and the systemic deliveryof adeno-associated virus. Both of thesetechnologies do not depend on mating, andhence can perturb the molecules and cellsof interest to test hypotheses in vivo withina couple of weeks in mice. Identificationof cellular circuits will further facilitate theanalysis, control and even design of cellular
networks.
Nature ReviewS | NEURoSCiEnCE

Regarding lines of research, I am
particularly excited about the transhierarchical studies to investigate the roleof particular residues of specific moleculesin specified cells in organisms, which are
currently only possible for limited modelorganisms such as nematodes and fruit flies.
This is because the mysteries of biologicalsystems often come from unique andirreplaceable molecules and/or cells thatplay essential roles in the systems. To fullyand deeply understand the uniqueness andirreplaceability of an essential molecule, it
is important to investigate its underlying
atomic network. Likewise, to elucidate
those of a pivotal cell, it is key to study
its underling molecular network. Twenty
years from now, by the 40th anniversary of
Nature Review Neuroscience, I hope that suchtrans-hierarchical identification, analysis,control and design of atomic, molecularand cellular networks will be possible inhigher organisms, which will eventuallylead to a wider and deeper understanding oforganismal systems in mammals, primatesand even humans.
Danielle S. Bassett 1,2,3,4,5,6 ✉,Kathleen E. Cullen 7,8,9 ✉, Simon B. Eickhoff 10,11 ✉,Martha J. Farah 12 ✉, Yukiko Goda 13 ✉,Patrick Haggard 14 ✉, Hailan Hu 15,16 ✉,Yasmin L. Hurd 17,18 ✉, Sheena A. Josselyn 19,20,21 ✉,Baljit S. Khakh 22 ✉, Jürgen A. Knoblich 23 ✉,Panayiota Poirazi 24 ✉, Russell A. Poldrack 25 ✉,Marco Prinz 26,27,28 ✉, Pieter R. Roelfsema 29,30,31 ✉,Tara L. Spires-Jones 32 ✉, Mriganka Sur 33 ✉ andHiroki R. Ueda 34,35 ✉1
Department of Bioengineering, University ofPennsylvania, Philadelphia, PA, USA.

Department of Electrical and Systems Engineering,
University of Pennsylvania, Philadelphia, PA, USA.
2

Department of Physics and Astronomy, Universityof Pennsylvania, Philadelphia, PA, USA.
3

15
Department of Psychiatry of First Affiliated Hospital,
Zhejiang University School of Brain Science and Brain
Medicine, Zhejiang University School of Medicine,
Hangzhou, China.
16
The MOE Frontier Research Center of Brain & Brain–
Machine Integration, Zhejiang Laboratory for Systems& Precision Medicine, Hangzhou, China.
17
Department of Psychiatry, Icahn School of Medicine
at Mount Sinai, Addiction Institute of Mount Sinai,
New York, NY, USA.
18
Department of Neuroscience, Icahn School of
Medicine at Mount Sinai, Addiction Institute of Mount
Sinai, New York, NY, USA.
19
Program in Neurosciences & Mental Health, Hospitalfor Sick Children, Toronto, Ont., Canada.
20
Department of Psychology, Physiology & Institute ofMedical Sciences, University of Toronto, Toronto, Ont.,
Canada.

Brain, Mind & Consciousness Program, Canadian
Institute for Advanced Research (CIFAR), Toronto, Ont.,
Canada.

21

22
Department of Physiology, David Geffen School of
Medicine, University of California Los Angeles,
Los Angeles, CA, USA.
23
Institute of Molecular Biotechnology of the Austrian
Academy of Sciences (IMBA), Vienna BioCenter (VBC),
Vienna, Austria.
24
Institute of Molecular Biology and Biotechnology(IMBB), Foundation of Research and Technology-Hellas(FORTH), Heraklion, Crete, Greece.
25
Department of Psychology, Stanford University,
Stanford, CA, USA.
26
Institute of Neuropathology, Medical Faculty,
University of Freiburg, Freiburg, Germany.
27
Signalling Research Centres BIOSS and CIBSS,
University of Freiburg, Freiburg, Germany.
28
Center for Basics in NeuroModulation(NeuroModulBasics), Faculty of Medicine, University
of Freiburg, Freiburg, Germany.
29
Department of Vision and Cognition, Netherlands
Institute for Neuroscience, Amsterdam, Netherlands.
30
Department of Integrative Neurophysiology,
VU University, Amsterdam, Netherlands.

4

Department of Psychiatry, Academic Medical Centre,
Amsterdam, Netherlands.

Department Psychiatry, University of Pennsylvania,
Philadelphia, PA, USA.

32
Centre for Discovery Brain Sciences and UK
Dementia Research Institute, University of Edinburgh,
Edinburgh, UK.

Department of Neurology, University of Pennsylvania,
Philadelphia, PA, USA.
5

Santa Fe Institute, Santa Fe, NM, USA.

6

Department of Biomedical Engineering, Johns Hopkins
University, Baltimore, MD, USA.
7

Department of Otolaryngology — Head and Neck
Surgery, Johns Hopkins University, Baltimore, MD, USA.

8

Department of Neuroscience, Johns Hopkins
University, Baltimore, MD, USA.
9

Institute of Systems Neuroscience, Medical Faculty,
Heinrich Heine University Düsseldorf, Düsseldorf,
Germany.

10

Institute of Neuroscience and Medicine, Brain &Behaviour (INM-7), Research Centre Jülich, Jülich,
Germany.

11

12
Center for Neuroscience & Society, University ofPennsylvania, Philadelphia, PA, USA.
13
Laboratory for Synaptic Plasticity and Connectivity,
RIKEN Center for Brain Science, Wako-shi, Saitama,
Japan.
14
Institute of Cognitive Neuroscience, University
College London, London, UK.

31

33
Picower Institute for Learning and Memory,
Department of Brain and Cognitive Sciences,
Massachusetts Institute of Technology, Cambridge,
MA, USA.
34
Department of Systems Pharmacology,
The University of Tokyo, Tokyo, Japan.
35
Laboratory for Synthetic Biology, RIKEN BDR, Suita,
Osaka, Japan.

✉e-mail: dsb@seas.upenn.edu; kathleen.cullen@
jhu.edu; simon.eickhoff@uni-duesseldorf.de; mfarah@
psych.upenn.edu; yukiko.goda@riken.jp; p.haggard@
ucl.ac.uk; huhailan@zju.edu.cn; yasmin.hurd@mssm.edu; sheena.josselyn@sickkids.ca; bkhakh@
mednet.ucla.edu; juergen.knoblich@imba.oeaw.ac.at;poirazi@imbb.forth.gr; poldrack@stanford.edu;
marco.prinz@uniklinik-freiburg.de; p.roelfsema@nin.knaw.nl; tara.spires-jones@ed.ac.uk; msur@
mit.edu; uedah-tky@umin.ac.jp
https://doi.org/10.1038/s41583-020-0363-6

Published online 2 September 2020
volume 21 | October 2020 | 533

Viewpoint
Acknowledgements
D.S.B. is grateful to the mentees, colleagues and mentors whoconstantly broaden her appreciation of the diversity of scientific inquiry and deepen her excitement to pursue the mostpuzzling questions. K.E.C. was supported by the National
Institutes of Health (NIH; grant numbers DC002390,
DC018061 and UF1 NS111695). S.B.E. is supported by theDeutsche Forschungsgemeinschaft (DFG; EI 816/16-1 and EI
816/21-1) and the European Union’s Horizon 2020 Researchand Innovation Programme under Grant Agreement No.
945539 (HBP SGA3) and 826421 (Virtual Brain Cloud). Y.G.
is supported by funds from the RIKEN Center for Brain
Science, The Uehara Memorial Foundation and the Japan
Society for the Promotion of Science (JSPS) Core-to-Core
Program A Advanced Research Networks. P.H. is partly supported by a joint grant from the John Templeton Foundationand the Fetzer Institute. The opinions expressed in this publication are those of the author(s) and do not necessarilyreflect the views of the John Templeton Foundation or the
Fetzer Institute. Furthermore, P.H. was supported by a Chairede Recherche Jean D’Alembert held at Paris-Saclay University,and from a fellowship at the Paris Institute for Advanced
Study (France), with the financial support of the French State,programme ‘Investissements d’avenir’ managed by the
Agence Nationale de la Recherche (ANR-11-LABX-0027-01
Labex RFIEA+). H.H. is supported by grants from the National
Natural Science Foundation of China (31830032 and81527901) and the Fountain-Valley Life Sciences Fund of
University of Chinese Academy of Sciences Education
Foundation. Y.L.H. is supported by grants DA050323,
DA048613, DA043247 and DA030359 from the National
Institute of Drug Abuse (NIDA). S.A.J. acknowledges discussions with P. W. Frankland (SickKids) and members of the
Josselyn and Frankland laboratories for continued inspiration. B.S.K. is supported by the NIH (NS111583 and534 | October 2020 | volume 21

MH104069), CHDI Inc., an Allen Distinguished InvestigatorAward through The Paul G. Allen Frontiers Group, a UCLADavid Geffen School of Medicine Seed Grant and the Ressler
Family Foundation. J.A.K. would like to thank all his lab members for exciting discussions. Work in J.A.K.’s laboratory is
supported by the Austrian Federal Ministry of Education,
Science and Research, the Austrian Academy of Sciences, theAustrian Science Fund (Z_153_B09), the City of Vienna andthe European Research Council (ERC, grants 695642
and 693184). P.P. thanks the members of her laboratory(www.dendrites.gr) for their dedication and hard work. Herlaboratory is funded by the European Union’s Horizon 2020
research and innovation programme (FET Open RIA GA
863245: NEUREKA, MC RISE GA 873178 INAVIGATE), the
EINSTEIN Foundation-Berlin and the Fondation Santé. M.P.
is supported by the Sobek Foundation, the Ernst-Jung
Foundation, the DFG (SFB 992, SFB1160, SFB/TRR167,
Reinhart-Koselleck Grant and Gottfried Wilhelm Leibniz Prize)
and the Ministry of Science, Research and Arts, BadenWuerttemberg (Sonderlinie ‘Neuroinflammation’). M.P. is further supported by the DFG under Germany’s Excellence
Strategy (CIBSS — EXC-2189 — Project ID390939984).
P.R.R. was supported by the Netherlands Organization for
Scientific Research (NWO; grant P15-42 ‘NESTOR’), theEuropean Union’s Horizon 2020 Framework Programme
Agreement No. 650003 (HBP FPA) and the Friends
Foundation of the Netherlands Institute for Neuroscience.
T.L.S.-J. is funded by the European Research Council (ERC)
under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 681181), the UK
Dementia Research Institute — which receives its funding
from DRI Ltd, funded by the UK Medical Research Council,
Alzheimer’s Society and Alzheimer’s Research UK — and the
University of Edinburgh. M.S. thanks the members of his laboratory for their comments. Research in the laboratory of

M.S. is supported by NIH grants EY028219, MH085802
and DA049005, and the Simons Foundation Autism
Research Initiative. H.R.U. is supported by Brain/MINDS
JP21dm0207049, Science and Technology Platform Programfor Advanced Biological Medicine JP21am0401011,
Grant-in-Aid for Scientific Research (S) JP25221004(JSPS KAKENHI) and HFSP Research Grant Program(HFSP RGP0019/2018).

Author contributions
The authors contributed equally to all aspects of the article.

Competing interests
P.R.R. is a co-founder of and shareholder in a neurotechnology start-up, Phosphoenix (Netherlands). T.L.S.-J. receives
collaborative funding from three industry partners (Autifony,
Cognition Therapeutics and one anonymous industry donor),is on the Scientific Advisory Board of Cognition Therapeutics
and is an editor for several journals, including founding Editor
in Chief of Brain Communications. None of these influenced
this piece. D.S.B., K.E.C., S.B.E., M.J.F., Y.G., P.H., H.H., Y.L.H,
S.A.J., B.S.K., J.A.K., P.P., R.A.P., M.P., M.S. and H.R.U.
declare no competing interests.

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