[Comp-neuro] Re: Attractors, variability and noise

James A. Bednar jbednar at inf.ed.ac.uk
Thu Aug 14 01:39:05 CEST 2008


|  From: comp-neuro-bounces at neuroinf.org On Behalf Of jim bower
|  Sent: 01 August 2008 13:45
|  To: bard at math.pitt.edu
|  Subject: Re: [Comp-neuro] From Socrates to Ptolemy
| 
|  But, biology is different and the difference and the conflict is
|  perhaps best indicated in the difference between abstracted models
|  and "realistic" models.
| 
|  First, I would define realistic models not only as those that
|  include as much of the actual structure as possible, but also and
|  perhaps most importantly, models that are "idea" nuetral in their
|  construction.

Hi Jim,

Your written definition of a "realistic" model sounds reasonable to
me, but in practice it seems like you only consider multicompartmental
Hodgkin/Huxley models to be realistic (based on this discussion and
your talks at previous GUM/WAM-BAMM meetings).  Is that true?

I agree that multicompartmental models are expressed at the right
level for addressing questions about single neurons.  However, I can't
agree that such models are "idea neutral" or "not abstracted", because
in practice they rely on an idea that the rest of the brain can
reasonably be modeled extremely abstractly, e.g. as a set of spike
trains following some simple distribution.  Such an assumption only
makes sense to me if the research questions are all about the behavior
of that one neuron, rather than the coordinated behavior of the large
populations of neurons that underlie functions like early vision in
mammals (my own area of interest).

For understanding visual processing, "including as much of the actual
structure as possible" means including the hundreds of thousands or
millions of neurons known to be involved.  When it is not possible to
build tractable multicompartmental models of that many neurons (due to
the enormous numbers of free parameters and the computational
complexity), there is no way to avoid relying on an idea or
abstraction.  One must choose either to simplify the neuronal context
(by modeling only a few neurons at a very detailed level), or to
simplify the models of each neuron.  Someday perhaps we might not have
to make such a choice, but certainly today and for the foreseeable
future we do.

Which option to choose, then, depends on the question being asked,
which depends on the experimental techniques available and on the
modelers' "ideas" and "abstractions" about what is important.  If
trying to understand and explain detailed measurements of single
neurons, then sure, build multicompartmental models of single neurons,
with an embarrassingly simplified representation of the rest of the
brain (and of the environmental and behavioral context).  But if
trying to understand data from large neural populations, e.g. from
2-photon calcium imaging of patches of hundreds or thousands of
visually driven neurons, or from optical imaging of even larger areas,
then a "realistic" model should contain huge numbers of simpler models
of the individual neurons, so that the large-scale behavior can be
matched to the data available.  Such forms of imaging data don't
provide any meaningful constraints on the parameters of a
multicompartmental model, but they do match very well to simplified
single-compartment (point-neuron) models.  Thus I would argue that
very simple point-neuron models can be appropriate and "realistic", if
they are embedded in a large network whose behavior is closely tied to
imaging results at that level (as in my own current work).

Of course, one could argue that large-scale imaging methods that only
measure firing rates or average membrane potentials (and indirectly at
that!) are simply irrelevant, precisely because they don't capture
enough of the behavior of individual neurons to constrain a
compartmental model.  But that's an idea about what is important and
what is ok to ignore, just like my own opinion that neural behavior
only makes sense in the context of large populations.

Jim
_______________________________________________________________________________
Dr. James A. Bednar
Institute for Adaptive and Neural Computation
University of Edinburgh
http://homepages.inf.ed.ac.uk/jbednar

-- 
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.



More information about the Comp-neuro mailing list