[Comp-neuro] "realistic models"

Thomas Trappenberg tt at cs.dal.ca
Mon Aug 18 15:13:13 CEST 2008

Let me add to Bard's and Carson's wonderful and well formulated thoughts my
own more pragmatic view. As modelers, we continuously face the struggle with
the level of abstraction, and it is sometimes easier to add more details
than to argue about simplifications.

Maybe it is useful to realize better the meaning of a model. I added an
attempt of a brief definition and discussion of a model in the first chapter
of my my book 'Fundamentals of Computational Neuroscience':
"Models are abstractions of real world systems or implementations of
hypothesis to investigate particular questions about, or to demonstrate
particular features of, a system or hypothesis".
Models are thus not intended to be the real thing. They should simulate
specific aspects of the real thing to answer specific questions.

As an example, I imagine a building engineer who wants to test the
robustness of a bridge. Ultimately it is the real bridge that should be
tested, but it is much more practical and chaeper, and often more
insightful, to test a more abstract version that models specific static
aspects of the bridge and neglects, for example, aesthetic details of the
construction. In contrast, an architect wants to build a model to test
aesthetic aspects while safely neglecting static properties.

If we want to test the propagation features of dendritic currents, we need,
of course, extended neuron models, but I find also more abstract models
insightful, often more so than spiking models, to guide our thinking of
cognitive processes. We do not know how much the propagation details in
dendrites are effecting emerging cognitive processes. It is not enough to
argue that they will influence the precise state of the system, but are they
essential to understand how specific cognitive processes work?

I think we will continue to struggle with these questions, but we need to be
careful to appreciate the contributions from different levels of

Sincerely, Thomas Trappenberg

On Sun, Aug 17, 2008 at 10:30 PM, G. Bard Ermentrout <bard at math.pitt.edu>wrote:

> Carson Chow, a former colleague,  has an interesting summary of this
> doscussion on
> sciencehouse.blogspot.com
> - Years ago Carson and I would go to neuro lectures (which I generally find
> far more accessible than math colloquia - and I am a professional
> mathematician! - which speaks on the issue that I think it is far easier
> form a mathematician  to gain an appreciation for biology than vice versa,
> but I digress) and there were a number on the complex channels found in
> dendrites which from an evolutionary point of view, must be quite costly.
> However, in almost all the cases, the final point of the speaker was that
> this was to compensate fro being out at the end of the dendrite, so that we
> used to say that nature is trying to make all neurons point neurons.
> Computationally, we can put as many inputs as we want into a point - but
> anatomy and physiology prevent this in real cells, hence the complex
> structure.
> - This leads to a second point - the neural turing test. (There have been
> contests related to this). I recently heard Eugene Izhikevich give a talk
> and he showed a picture of a recording froma cortical pyramidal cell
> receiving a complex stimulus pattern (whatever that means, Jim) and his 2
> variable 4 parameter model - the sub and super threshold behavior was almost
> indistinguishable and this model was fit for the FI curve only. I realize
> that the pyramidal cell stimulus was quite simplistic, but one could
> presumably do the same stim mixed with other stimuli in the dendrites. maybe
> there are complex dendritic calculations going on - but the bottom line is
> what is the output of the cell - that is all that matters. So any model that
> does this in a reasonable way will, to me, be a realistic model since the
> cell on the other side of the wall cannot distinguish it. I would guess that
> Jim Bower would claim there is no such model that does this except the most
> delatiled model with all the channels and structure. However, I am less
> pessimistic about this for the following reason:
> -Yannis Kevrikides has deveolped some very useful numerical tools that
> exploit a common freature in many complex physical and biological systems
> (here, I am a strong reductionist and believe with every fiber of my body
> that biology is describable by physical pronciples - I lost all shreds of
> mysticism in Nov 1969 - although I continued to exploit others making money
> casting horoscopes - a mathematical exercise, in fact)
> basically, most systems, even complex ones, behave in such a manner as to
> drastically reduce dimensionality. They are strongly contracting or
> dissipative and as a consequence, are captured by far fewer degrees of
> freedom. Kevrikides methods allow one to compute in these lower degrees
> without knowing the underlying reduced equations. Nevertheless, they are
> there. Mathematicians and physicists have used these ideas for years and
> call it averaging, mean field reduction, etc and of course experimentalists
> do use these ideas as well and call it PCA in which they show that only a
> few modes capture the majority of the variance. Thus, the Turing test neuron
> is not pie in the sky and I believe that there are reduced models that will
> do what the "realistic" model does with as much precision as you would like.
> Regards
> Bard Ermentrout
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> Comp-neuro at neuroinf.org
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