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<p class="MsoNormal"><span style="font-size: 11pt; font-family: Arial;">This
post
provides a view of the disagreement about whether there is a comp-neuro
paradigm. <o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size: 11pt; font-family: Arial;">It
argues
that there is, but that in light of new data we can no longer believe
the
paradigm’s core assertion, around which the field is organized, that a
structural
foundation underlying input-output electrophysiology is the fundamental
unit
for brain analysis. <o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size: 11pt; font-family: Arial;">The
comp-neuro paradigm is well-entrenched in this forum and in the
homogeneous culture
of communities that gather for the CNS meeting, CRCNS reviews, as
NEURON-GENESIS
users, etc. The paradigm formalizes the data and ideas developed in the
classical era of neuroscience, extending into the early 80's. This era
was
dominated by experiments on tract-tracing/connectionism,
neurophysiology and
channels, and these were motivated by the ideas of that time as to 'how
the
brain works', as in the following 3 premises:<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size: 11pt; font-family: Arial;">(1)
The
neuron is the fundamental unit for analysis, a neuron's input-output
membrane
electrophysiology defines its function, and this arises from the
detailed,
precise structure of the neuron’s somatic morphology, dendrite
morphology and
synapse-channel locations on same.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size: 11pt; font-family: Arial;">(2)
These specific
neuronal structures are essentially structurally fixed, and are located
in
functionally specific connectional circuit architectures.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size: 11pt; font-family: Arial;">(3)
Neuronal structure and circuit architecture are the fingerprint of
natural
selection, the result of evolution’s genetic program and thus it all
“matters”
- the brain is “Kolmogorov Complexity Complete” (KCC) - at a first
approximation
brain function (traits, behaviors, minds) arises from the naturally
selected,
structurally fixed, functionally specific structures as
instinctive-ethological
type outputs, approaching fixed action patterns.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size: 11pt; font-family: Arial;">The
comp-neuro paradigm has been immensely productive, arguably the most
successful
use of modeling within biology. However, equally arguably, progress has
greatly
slowed. Worse, its agenda now, at the limit, calls for ‘KCC complete’
type
goals that are heroic if not completely out of bounds. Worst of all,
the value
in completing these goals appears questionable in light of more
contemporary
approaches such as systems biology, which for example shows that great
'biological variability (noise)' is expected, generates robust systems,
and is normal
among and within neurons. <o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size: 11pt; font-family: Arial;">Even
more
important than the above difficulties within the comp-neuro
paradigmatic
structure itself is the fact that, from the early 80s on, neuroscience
has
undergone a technical revolution that has produced a wealth of new data
and
approaches, and these new data and ideas are not accounted for in the
comp-neuro paradigm. For example:<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size: 11pt; font-family: Arial;">(1)<span
style=""> </span>Work in contemporary neuroscience emphasizes
receptor complexity leading to combinatorially derived phenotype or
function, including
non-synaptic inputs and dominated by inputs not coupled to ion
channels,
without direct electrophysiological effects. By these mechanisms
environmental
experience is read and encoded. A neuron’s receptor profile has
downstream
effects on signaling networks, gene networks and function. All these
layers are
interconnected by feedback, and each layer (signaling, genes,
receptors,
neurons etc.) is organized in networks that have nonlinear dynamics.
The
functional impact includes channel state, type, density, location but
also has
many dimensions beyond electrophysiology. If all this is true, then the
first
premise above is not.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size: 11pt; font-family: Arial;">(2)
Directly
related to this is the known plasticity in modulators, networks and
cellular
physiology such that neurons are constantly changing, not in a
structural
steady state. This produces flexibility and variability in structural
organization and in function. Similarly, at a higher level, the work of
Mezernich
and of Greenough and others on brain plasticity-remodeling comes to
mind here,
as well as the emerging literature on adult stem cells. If all this is
true,
then the second premise above is not.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size: 11pt; font-family: Arial;">(3)
The
evidence from functional genomics and signaling systems likewise is not
consistent with a structurally committed brain, but rather one using
diverse
strategies and ongoing remodeling, nor are recent epigenomic
developments,
suggesting rapid, current evolution of traits. The kind of genetic
determinism
inherent in the comp-neuro paradigm is naïve. If all this is true, then
the
third premise is not.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size: 11pt; font-family: Arial;">A
new
computational paradigm for neuroscience arguably not only has to
account for
these and other new data but also needs to develop a guiding concept of
'how
brains work' that organizes the data, putting adaptive remodeling and
fungible interactions
of nonlinear dynamical networks in functional process in a central role.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size: 11pt; font-family: Arial;"><o:p> </o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
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