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

Antonio Carlos Roque da Silva Filho antonior at ffclrp.usp.br
Fri Aug 15 20:59:47 CEST 2008

Jim and others,

Regarding analogies, some of you may find interesting my own analogy between 
brain models and artistic styles.

Artists (painters, sculptors, writers, photographers, flm directors, etc) 
have their own way to "model" human nature (a consequence of the human 
brain) and they have been doing this for much longer than computational 
neuroscientists, so we may learn something from them. They also debate 
strongly on the appropriate level of approaching nature, and their works are 
usually classified as impressionistic, expressionistic or realistic.

I suggest that brain models also can be classified into this tripartite 

Impressionistic neural models would be those which use reduced neuron 
models, i.e. based on a small number of parameters. Such models convey the 
"impression" of reality. Some examples (with varying impressionistic 
degrees) are models that use single neuron models like Hindmarsh-Rose's, 
FitzHugh-Nagumo's, Izhikevich's, integrate-and-fire, cellular automata, 
McCulloch-Pitts, etc. These models attempt at an understanding (in a 
dynamical systems sense) of the system under study. So far, they have been 
very successful in providing such a kind of understanding for single neuron 
spiking behavior but their usefulness for large-scale brain models (other 
than speeding up simulations) has yet to be shown.

Expressionistic neural models would be those that "express" their authors' 
views on the brain. They start from a number of a priori ideas about the 
brain and, ideally (but not always), should be constructed so that these 
ideas are explicitily present in the models so that they can be tested 
experimentally. The most famous example of such models is Freud's 
psychoanalysis (btw, his first attempt was to construct a biology-based 
model but due to lack of data at
his time he ended up turning to the other end of the spectrum) but one also 
may include in this class models based on hypotheses like Hebb's cell 
assemblies, synfire chains, Freeman's K sets, chaotic itinerancy, etc. The 
main characteristic of these models (for the good and the bad as has been 
extensively discussed in this list) is that they propose interpretations of 
the brain functions.

Realistic neural models would be those that attempt to reproduce structural 
and physiological properties of cells and circuits in an accurate way. 
Examples are HH-type models, compartmental models, etc. In principle, they
should be idea-free, as Jim pointed out, so that they place value on the 
available experimental data. To put it another way, they are constructed and 
can be used to test how good is one's choice of data to reproduce a given
behavior. The appeal of this kind of modeling is that, given the high degree 
of brain complexity which exceeds our capacity of understanding it, they may 
be the only option ahead. But this is not to be considered a drawback. The 
demand for realism in art led to a lot of useful knowledge like, e.g., 
better anatomical descriptions, the laws of perspective, lighting techniques 
and representation of movement (think of modern animation films). In film, 
to cite another art analogy, several directors agree that the ideal to be 
achieved is pure realism, like in a documentary: it would be shot without 
any a priori explanatory attempt but, after being seen, would have explained 
a lot.


PS. Jim, thank you for publicizing LASCON. I wish I had the means to bring 
some of the guys who have been discussing here to have an open discussion on 
these issues with our students in 2010.

Antonio Roque
Departamento de Fisica e Matematica
FFCLRP, Universidade de Sao Paulo
14040-901 Ribeirao Preto-SP
Brazil  -  Brasil

Tels: +55 16 3602-3768 (office);
         +55 16 3602-3859 (lab)
FAX: +55 16 3602-4887
E-mails: antonior at neuron.ffclrp.usp.br
             antonior at ffclrp.usp.br
URL: http://neuron.ffclrp.usp.br

----- Original Message ----- 
From: "james bower" <bower at uthscsa.edu>
To: "Brad Wyble" <bwyble at gmail.com>
Cc: "CompNeuro List" <comp-neuro at neuroinf.org>
Sent: Wednesday, August 13, 2008 4:23 PM
Subject: Re: [Comp-neuro] Re: Attractors, variability and noise

>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 -
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