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Building Models of the Brain
If we were magically shrunk and put into someone's brain while she was thinking, we would see all the
pumps, pistons, gears and levers working away, and we would be able to describe their workings completely,
in mechanical terms, thereby completely describing the thought processes of the brain. But that description
would nowhere contain any mention of thought! It would contain nothing but descriptions of pumps, pistons,
levers!
—G.
W.
L
EIBNIZ
(1646–1716)
How do ... fields express their principles? Physicists use terms like photons, electrons, quarks, quantum wave
function, relativity, and energy conservation. Astronomers use terms like planets, stars, galaxies, Hubble
shift, and black holes. Thermodynamicists use terms like entropy, first law, second law, and Carnot cycle.
Biologists use terms like phylogeny, ontogeny, DNA, and enzymes. Each of these terms is actually the title of
a story! The principles of a field are actually a set of interwoven stories about the structure and behavior of
field elements.
—P
ETER
J.
D
ENNING
,
P
AST
P
RESIDENT OF THE
A
SSOCIATION FOR
C
OMPUTING
M
ACHINERY
,
IN
"G
REAT
P
RINCIPLES OF
C
OMPUTING
"
It is important that we build models of the brain at the right level. This is, of course, true for all of our scientific
models. Although chemistry is theoretically based on physics and could be derived entirely from physics, this would
be unwieldy and infeasible in practice. So chemistry uses its own rules and models. We should likewise, in theory, be
able to deduce the laws of thermodynamics from physics, but this is a far-from-straightforward process. Once we have
a sufficient number of particles to call something a gas rather than a bunch of particles, solving equations for each
particle interaction becomes impractical, whereas the laws of thermodynamics work extremely well. The interactions
of a single molecule within the gas are hopelessly complex and unpredictable, but the gas itself, comprising trillions of
molecules, has many predictable properties.
Similarly, biology, which is rooted in chemistry, uses its own models. It is often unnecessary to express higher-
level results using the intricacies of the dynamics of the lower-level systems, although one has to thoroughly
understand the lower level before moving to the higher one. For example, we can control certain genetic features of an
animal by manipulating its fetal DNA without necessarily understanding all of the biochemical mechanisms of DNA,
let alone the interactions of the atoms in the DNA molecule.
Often, the lower level is more complex. A pancreatic islet cell, for example, is enormously complicated, in terms
of all its biochemical functions (most of which apply to all human cells, some to all biological cells). Yet modeling
what a pancreas does—with its millions of cells—in terms of regulating levels of insulin and digestive enzymes,
although not simple, is considerably less difficult than formulating a detailed model of a single islet cell.
The same issue applies to the levels of modeling and understanding in the brain, from the physics of synaptic
reactions up to the transformations of information by neural clusters. In those brain regions for which we have
succeeded in developing detailed models, we find a phenomenon similar to that involving pancreatic cells. The models
are complex but remain simpler than the mathematical descriptions of a single cell or even a single synapse. As we
discussed earlier, these region-specific models also require significantly less computation than is theoretically implied
by the computational capacity of all of the synapses and cells.
Gilles Laurent of the California Institute of Technology observes, "In most cases, a system's collective behavior is
very difficult to deduce from knowledge of its components....[Neuroscience is ... a science of systems in which first-
order and local explanatory schemata are needed but not sufficient." Brain reverse-engineering will proceed by
iterative refinement of both top-to-bottom and bottom-to-top models and simulations, as we refine each level of
description and modeling.
Until very recently neuroscience was characterized by overly simplistic models limited by the crudeness of our
sensing and scanning tools. This led many observers to doubt whether our thinking processes were inherently capable
of understanding themselves. Peter D. Kramer writes, "If the mind were simple enough for us to understand, we would
be too simple to understand it."
50
Earlier, I quoted Douglas Hofstadter's comparison of our brain to that of a giraffe, the
structure of which is not that different from a human brain but which clearly does not have the capability of
understanding its own methods. However, recent success in developing highly detailed models at various levels—from
neural components such as synapses to large neural regions such as the cerebellum—demonstrate that building precise
mathematical models of our brains and then simulating these models with computation is a challenging but viable task
once the data capabilities become available. Although models have a long history in neuroscience, it is only recently
that they have become sufficiently comprehensive and detailed to allow simulations based on them to perform like
actual brain experiments.
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