[Comp-neuro] Hilbert's questions
axel.hutt at loria.fr
Wed Aug 13 10:38:14 CEST 2008
> As far as the grande (sic) questions in neuroscience go, at the
> grandest level it seems to me there is only one question: "What is
> each neuron communicating, and how is the message encoded."
> Once we know that, the rest is clean up.
as a physicist by education but working in neuroscience, I am really
puzzled about the approach to start from the single neuron to understand
information processing. For me, this seems as if one aims to understand
water waves, hydrodynamic convection or turbulence in fluids by studying
the collisions of the fluid molecules or the dynamics of the atoms.
In physics, many hydrodynamic phenomena are well-described by
macroscopic equations (e.g. the Navier-Stokes equation). The reason
for the power of the macroscopic equation is the optimal choice of the
description level. There are also good microscopic models for the
intra-molecular dynamics of fluids and some for the interaction between
molecules. However transversing the scales is very difficult, though
the research starts from each description level (maacro and micro)
to find the bridge between the scales.
In neuroscience, we have a similar problem of scales, while the (living)
neural systems are complex compared to fluids. However, in
neuroscience the choice of the description level is also very important
and IMHO one should aim to find reasonable descriptions for each level.
Currently, macroscopic models such as neural population models assuming
an underlying population rate code allow to reproduce successfully
rather macroscopic activities (EEG/MEG,LFP). However, much work has to
be done to include spatial topologies. Further network models
built of spiking neurons (time-coding models) allow for the successfull
description on the single neuron level. Each spatial/temporal level
allows for the reasonable description of specific phenomena, e.g.
macroscopic activity is strongly related to cognitive phenomena and
some first stages in perception, while microscopic activity may
reflect the neural performance in time-related tasks and some temporal
aspects of perception. Hence each description level has its own meaning
and importance and it may be a good idea to keep working on improved the
models for each level and scale before combining the scales.
In the ongoing discussion, somebody wrote (sorry, do not know anymore
who) that neuroscience research is in some way in the situation where
physics was in the 16th century. I fully agree. Though we measure
plenty of data and have new exciting and powerful techniques to measure
activity in the brain, sometimes we are far from understanding what is
going on in the brain. Hence I suggest to focus on improving the
models in the respective scale to describe the respective experimental
Some problems may be:
* how does the spatial topology of visual areas in mammals affect the
processing of visual information ? Are extended rate-coding models
sufficient to describe the corresponding major underlying mechanisms
including the intra-area connections and the inter-area feedback loops?
* what is an optimal model approach to describe time-coding effects
in networks including different types of synapses, dendritic trees and
the axonal branching system ?
INRIA CR Nancy - Grand Est
CS20101, 54603 Villers-lès-Nancy Cedex
More information about the Comp-neuro