Connectionists: "Abstract" vs "Biologically realistic" modelling

[Brad Wyble]

I'd like to throw in a few cents.


>  However, if you want to claim that your model also reveals something
> important about how brains work, then the model must either be 'realistic'
> first, or be able to link to such a model.
>
>
I think that whether this is true depends on your definition of "how brains
work".  For my purposes, the litmus test of a model that reflects "how
brains work" in some fundamental sense is reflected in their ability to
generate novel predictions that are correct.  Your warnings about
postdiction are right on target, since the predictions must be truly de
novo at the time they are created to ensure that you are not engaging in
curve fitting.

Therefore to me it seems as if we are using very different goals in the
modelling process. I want a model to reflect the behavior of the system,
and I use neural models because the added constraint of neural plausibility
enormously accelerates my search through the space of possible models by
preventing me from following innumerable dead ends (though there are still
quite a lot of neurally plausible dead ends).

Jim, you seem to want a model that reflects the wiring of the brain first
and foremost, and the functionality comes in a close second.  I think it's
fine for both of those goals to exist and I don't think it's necessary to
label one of them as useless, or even less efficient.

-Brad

PS.  Thanks for a very interesting debate!









> It goes without saying that these types of realistic models can be built
> at many levels, as long as the model has biological components.  (you won't
> convince me with mean field theories of cerebral cortex).  It also goes
> without saying, of course, that we don't have the technology or the
> knowledge for that matter to build one model of everything - although
> personally, I believe eventually we will have to, and reflecting that view,
> Version 3.0 of GENESIS was specifically built to link broadly across many
> different levels of scale.
>
> The critical question therefore, is whether the model is built in such a
> way that the biology can tell you something you didn't know before you
> started (just like the earth moon model told Newton) - or, is the biology
> just dressing up something you already believed to be true and just wanted
> to convince the rest of us.  Building the model out of realistic
> components, and then testing it on theory- neutral biological data, is more
> likely to lead to the former.  At least it has over and over again for us.
>
>
> Jim
>
>
>
> and in particular that your type of "realistic"
> multicompartmental single-cell and network modelling could ever do so.
>
> *Real* morphologically complex cells are embedded in complex networks,
> which are embedded in complex organisms, which are embedded in complex
> environments, which are embedded in complex ecosystems.  Evolution
> acts on the net result of *all* of this, indirectly via a process of
> development.  Certain species thrive in certain ecosystems if their
> proteins, cells, networks, nervous systems, bodies, and communities
> allow them to function in that environment well enough to reproduce.
> The details of *all* of these things matter.
>
> Are all of these details represented realistically in your models?
> No, and they shouldn't be -- you pose questions that can be addressed
> by the things you do include, abstract away the rest, and all is well
> and good.  But other different yet no less realistic models are built
> to address different questions, paying attention to different sets of
> details (such as large-scale development and plasticity, for my own
> models), and again abstract away the rest.
>
> I am happy to join with you to decry truly unrealistic models, which
> would be those that respect none of the details at any level.  Down
> with unrealistic models!  But there is no meaningful sense in which
> any model can be claimed to avoid abstraction, and no level that
> exclusively owns biological realism.
>
> Jim Bednar
>
> ________________________________________________
>
> Dr. James A. Bednar
> Director, Doctoral Training Centre in
> Neuroinformatics and Computational Neuroscience
> University of Edinburgh School of Informatics
> 10 Crichton Street, Edinburgh, EH8 9AB  UK
> http://anc.ed.ac.uk/dtc
> http://homepages.inf.ed.ac.uk/jbednar
> ________________________________________________
>
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> Scotland, with registration number SC005336.
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>
>
>
>
> Dr. James M. Bower Ph.D.
>
> Professor of Computational Neurobiology
>
> Barshop Institute for Longevity and Aging Studies.
>
> 15355 Lambda Drive
>
> University of Texas Health Science Center
>
> San Antonio, Texas  78245
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>
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>
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-- 
Brad Wyble
Assistant Professor
Psychology Department
Penn State University

http://wyblelab.com
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