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

james bower bower at uthscsa.edu
Wed Aug 13 21:23:29 CEST 2008


I would rather personally gain insights from including a known feature  
of the brain in a model than randomly misplacing parenthesis -  :-)

  perhaps a new application of genetic algorithms here - with respect  
to source code for abstract models.  :-)

However, the general point is absolutely taken -- without something  
concrete and mathematical, you don't know what you know or what you  
don't know.  Further, unless you share your model with others (and I  
don't mean through paper publication), they don't know what you know,  
they know, or collectively you don't know either.

Another problem with abstract models -- most of which are simple  
enough that you can write your own code.  Systems like Bard's XPP are  
absolutely essential to have some form of intercommunication -- and  
BTW,  what about misplaced parenthesis that go unrecognized?

Often when I talk to biologists about the need for modeling, they tell  
me that they don't yet know enough to build a model - truth is, you  
don't know how little you know until you start to build one (I may  
have already said that).

For sure I have said before (several times in several different ways)  
that a model should NEVER be principally designed to prove to people  
you are right (smart, sophisticated, or etc).  Unfortunately, many are.

Jim



On Aug 13, 2008, at 1:46 PM, Brad Wyble wrote:

>
> At the risk of missing my flight I can't resist continuing this  
> debate.
>
>
>
> As I understand the  other end of the spectrum, we construct  
> increasingly realistic models and end up with a simulated brain  
> without a real understanding of how it works, which makes no sense  
> to me.  Understanding is what we're after, and that understanding  
> can only reside in the brains of the population of scientists, not  
> in their models.
>
> I suspect that I have created a straw man here, but I'm curious to  
> what extent I've abused your position.
>
> Brad,
>
> Haven't abused at all -- with one big exception -- realistic models  
> are more likely to tell you how things work, than are models in  
> which 'how things work' is assumed.  In our experience, realistic  
> modeling has consistently and steadfastly told us things that we  
> didn't know before - problem is, those things fly in the face of  
> many of the current 'theories" operating in the parts of the brain I  
> study, making the publication of papers, getting grants, etc, much  
> more difficult.
>
> BTW, almost every time, the models have also made it clear that I  
> was wrong in how I was thinking about the system:
>
> I agree wholeheartedly but abstract models are just as capable of  
> telling us new things.  To cite a specific example of my own, my  
> current modelling effort (which explains a quite high-level  
> phenomenon of visual attention)  features a recurrent excitation  
> between targets and attention that I initially implemented by  
> misplacing a parenthesis in an equation.    I realized quite quickly  
> that this circuit worked better than the one I had intended to  
> create, and is just as plausible, if not more so, than what I had in  
> mind.
>
> So a major contribution of models is to allow us to explore the  
> behavior of systems more complicated than we can reason about in our  
> heads.  And it turns out that human reason hits its limit quite  
> quickly; even a model with a handful of abstract, rate-coded neurons  
> is informative in this respect.
>
>
> As for the holy grail of a realistic model of the entire brain, is  
> there such a thing as enough detail?
>
> I think that if a time traveller from the future dropped a  
> simulation of the brain, realistic down to the level of RNA  
> synthesis, in our laps, many of the realists would want to continue  
> drilling down to the sub molecular level and we'd be having the same  
> debate all over again.
>
> The rest of us would start trying to build abstract theories on top  
> of this simulation, so I think we might as well get started with  
> what we already know.
>
>
>
> There is hope
>
>
>
> Yes, however I think you have succesfully highlighted some glaring  
> difficulties with the way our discipline is currently running.    I  
> think the way out is not to focus on a particular end of the realism/ 
> abstract spectrum, but to do a better job of avoiding the tyrannical  
> ideas by focussing on data-driven theory.
>
> Hrmph, I think I have ended my short contribution to this debate  
> back where we started from.
>
> -Brad
>
>
>
>




==================================

Dr. James M. Bower Ph.D.

Professor of Computational Neuroscience

Research Imaging Center
University of Texas Health Science Center -
-  San Antonio
8403 Floyd Curl Drive
San Antonio Texas  78284-6240

Main Number:  210- 567-8100
Fax: 210 567-8152
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