Download Learning, Memory and Perception.

Learning, Memory and Perception.
(Erin Schuman and Gilles Laurent)
Most animals with a brain (including humans) use it ultimately to facilitate the
transfer of their genes (or those of their kin) to a next generation. Brains
“produce” innate behaviors (eating, fighting, fleeing, mating etc...), though much
of what brains do is interpret the environment, that is, extract features of potential
value for immediate and future use. Indeed, we can safely assume that brains
evolved to detect meaningful patterns (e.g., correlated rather than uncorrelated
motion), to learn, memorize and recall them, and to act adaptively. In a subset of
species, many of them social ones, brains can also produce and/or decode
communication signals. This deceptively simple constellation of features is the
emergent property of neuronal networks optimized by hundreds of millions of
years of evolution. Because animals, and thus brains, evolved on this planet,
they express also the selective biases imposed by the physics of our world and
environment: light-dark cycles, natural images and sounds, to take only a few
examples, are not randomly distributed; they have quite specific statistics—far
from randomness—to which our nervous systems are adapted. This adaptation
to the statistics of our physical world is another form of learning (be it on an
evolutionary time scale) expressed by today’s brains. Finally, and most
miraculously of all maybe, brains self-assemble, starting with just one cell and
ending sometimes with tens of billions as with humans, within every developing
individual. Within each developing brain one finds both the hidden biases that
result from natural selection (evolutionary “learning”), and the means to sculpt
each individual brain with its own, unique, life history.
Brains contain two main cell types: neurons, and support cells or glia. We will
focus here mainly on neurons, even though it is clear that glia play many
fundamental roles in the development, support, and plasticity of neural circuits.
Neurons comprise complex and extremely diverse cell types, all involved in
information transfer and processing. At the periphery, are sensory and motor
neurons. Sensory neurons transform some kind of energy (photons, pressure,
chemical binding etc...) into an electrical signal. Motor neurons cause, via
identical electrical signals, muscles to contract (in a few cases, to relax also). The
majority of neurons, however, are neither sensory nor motor- they exist in
between the input and the output. They are thus usually polarized cells, with
inputs at one end (called dendrites) and outputs at the other (called axons).
Neurons communicate with one another via specialized junctions called
synapses. Those can be electrical (allowing the direct flux of ions from one
neuron to the next) or chemical (necessitating the secretion and recognition of
chemical(s) on the pre- and post-synaptic sites of a junction, respectively). When
neuronal networks learn something about their environment there is an adaptive
regulation of the electrical properties of neurons and of their synapses. Because
chemical synapses are composed of many complex and interacting molecular
components, they are, with good reasons, the focus of most of the research on
the mechanisms of learning and memory. Understanding learning and memory
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thus implies understanding not only the basic properties of neurons and
synapses, but also discovering the learning rules underlying circuit modifications
and the mechanisms by which these learning rules can themselves be selected,
modified or adapted by ill-understood processes such as sleep and attention.
The complexity of this research thus lies in part on the multiplicity of scales (in
space and in time) over which interesting phenomena occur, and on the
existence of global properties that emerge only from cooperativity; reductionist
approaches alone are thus not sufficient. It is at an intermediate scale—that of
networks of interacting elements— that most discoveries remains to be made.
While these issues represent fascinating and challenging problems for science,
note also that malfunction of synapses and neural circuits are the causes or
consequences of most developmental neuropsychiatric and neuro-degenerative
diseases. Neurological diseases represent an enormous economical burden on
society, estimated at about 140 billion Euros for Europe alone in the year 2004. It
is through basic research on neuronal and synaptic function that we can hope to
shed light on the etiology of neurological diseases and ultimately, develop
modern and appropriate therapeutics.
Some Important Questions
1. How is the stability of memories achieved in a distributed system whose
elements (proteins, synapses, neurons) are constantly being remodeled?
Some of these elements are simply lost with age—after the age of 30, it is
estimated that humans lose some x neurons every day. Others, such as
constitutive proteins, simply turn-over, being actively degraded in a matter
of hours to weeks, and replaced in a presumably appropriate fashion, by
others which will assume the same role for, again, only a limited time. On
a larger scale, we know that the storage of memories shifts from one to
another location at different stages of their formation and consolidation. In
mammals, some of this transfer occurs over several weeks and appears to
depend on brain activity during certain phases of sleep. How do these
molecular, synaptic, cellular, network and dynamical phenomena all
interlock to generate memories, and ensure their long-term stability?
2. What features of spatio-temporal patterns in distributed networks give rise
to perception, storage and recall? Experimental results make it quite clear
that the perception of even the simplest objects must be the result of the
activation of millions of neurons in the human brain. These neurons are
distributed over and across areas, often on both sides of the brain and yet,
their activation leads to unified percepts. How does the brain, with its
interconnected network of billions of neurons, generate such unified
perception? Dynamics and temporal correlations are good candidate
mechanisms, although only in very rare cases, have conclusive results
been obtained. The recent development by MPG scientists, of molecular
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tools to manipulate the state of neurons using light, may allow some of
these hypotheses to be better tested. While neural representations are our
way to describe the neuronal substrates of percepts (for example, a rabbit,
a child’s voice, the smell of burning toast), they would be meaningless if it
were not for our ability to link them to corresponding memories: my seeing
a rabbit now is useful because I already know what a rabbit looks like and
because I am able to compare my present sensory experience to a
memory trace for rabbits that is already present in my brain. In other
words, perception, memory formation and recall, must all rely on
interlinked mechanisms and substrates. This is part of what makes brains
so different from modern computers. Memory and processing units are
one and the same.
Some Research Opportunities and Challenges
1. Reconnecting levels of inquiry. One of the most obvious characteristics of
the brain is that it derives its magic both from large-scale interactions
(properties of networks) and from very high degrees of specialization
expressed by its local constitutive elements. Neurons are indeed the most
diverse cell types in the body. Some specializations are visible to the
(nearly) naked eye: a hair cell in the cochlea differs dramatically from a
Purkinje cell. Others are subtler, but no less important: the number of
known principal neuron and interneuron subtypes in mammalian
neocortex, for instance, now exceeds many tens. These observations
(element diversity + emergent properties of assemblies) pose a major
practical problem: if what makes the brain so special indeed results from
these singularities on multiple scales, we should study networks, neurons
and molecular constituents in combination rather than in isolation (our
traditional approach). This will require a major evolution of our
experimental techniques towards scaling up (e.g., the numbers of
simultaneous samples), making compatible the tools and techniques that
have been used traditionally in isolation, and making sense of very high
dimensional datasets. These challenges will require the combined and
coordinated efforts of many specialties such as molecular biology,
genetics,
electrophysiology,
optics
and
imaging,
electronics,
nanotechnology, mathematics, computer science and nonlinear dynamics.
This is terrifically exciting, but it will require a new type of cooperativity
between areas of science that have often worked separately.
2. Exhaustive Connection and Molecular Mapping of Brain Circuits. The
remarkable development of new analytic tools for imaging and protein
chemistry, to take but two examples, now enables us to catalog and
describe the constituents of brains and networks to a remarkable degree.
While the task is clearly enormous, it offers no other major obstacle than
time and the optimization of data sampling, analysis and usability of
results. On a different level, the recent emergence of machine-vision-
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enhanced serial electron-microscopy, the development of multi-chromatic
genetic tools for neuronal labeling and the increased affordability of very
large computing power, make it possible to imagine a day when the
connection matrix of a small to medium sized brain (a fly’s to a mouse’s)
will be known to a very reasonable degree of accuracy. While this
knowledge will not, in and of itself, constitute understanding of the brain, it
will most likely be an essential stone on our path towards understanding.
Once again, multi-disciplinarity and very-large-scale datasets characterize
this research opportunity.
3. Behavior and Brain Activity. A major challenge for modern neuroscience is
to explain perception and behavior in terms of neural activity. Given the
size of the brain and the distributed nature of neural activity, it is becoming
increasingly clear that sparse sampling of activity (i.e., our traditional
method) is wholly inadequate. Techniques such as functional MRI give us
a coarse grain view of brain activity on a large scale, while patch-clamp
recordings allow us to sample one or a handful of neurons at very high
spatiotemporal resolution. But we really need to sample at the
“mesoscale”, at the population level, with very high sampling density and
in freely moving animals. This is a major technical challenge, that will rely
on major developments in the fields of optics, micro- and nano-electronics
and computer science.
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