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

james bower bower at uthscsa.edu
Fri Aug 15 18:46:54 CEST 2008

wonderful conversation

In reactors you aren't working with particles (neurons), whose  
performance and behavior depends on a force (selection) operating over  
millions of years or (perhaps) a force (learning) operating  
continually to produce unique individual solutions, that can only  
probably be understood in the context of the individual features of  
all the particles (neurons) involved.

imagine if you were.   :-)


Jim Bower

On Aug 15, 2008, at 11:29 AM, Igor Carron wrote:

> Robert,
> Your example of astronomy parallels many different kinds of  
> engineering fields. One that I am knowledgeable about is nuclear  
> engineering (nuclear engineering stands for the engineering that  
> goes into the building of nuclear reactors producing electricity).  
> There has been a similar tendency to build very complex models for  
> the past fifty years and then check these models at every level in  
> order to be confident that they are behaving as they should.  
> However, the more complex the models, the more likely the list of  
> parameters grows. Eventually, the growing complexity comes from the  
> integration of the physics of neutron transport with that of  
> computational fluid dynamics (orders of magnitude difference in  
> scale). This integration is required in order to make sure you  
> understand fully how a nuclear reactor core works. If you know  
> either, you know that the models have to be somewhat coarse because  
> there is no way you will be able to make out whether the new  
> integrated software can handle different known and unknown instances.
> This situation has led to several benchmark exercises where a  
> situation has been run on some experimental reactor and the findings  
> have been embargoed so that the different national labs would run  
> similar conditions and find out if their models were fitting these  
> very complex model responses.
> While this has led to a specific degree of confidence for situations  
> that are known, there is always some tweaking required by this or  
> that model to fit the latest benchmark exercise.
> Eventually, as in the brain question, the issue is how can one  
> become comfortable with a very complex system on one hand and a very  
> complex model on the other ?
> Can one map the expected behavior of a normal brain with the  
> expected behavior of a complex model for a large part of the  
> parameter space ? How do you explore a large part of the model  
> parameter space when you have a smaller set of brain data ?
> Can two different complex models yield similar results/behavior ? In  
> the affirmative, what criterion do you use to favor one over another ?
> Cheers,
> Igor.
> ------------------------
> Igor Carron, Ph.D.
> http://nuit-blanche.blogspot.com
> On Wed, Aug 13, 2008 at 1:23 PM, Robert Cannon <robert.c.cannon at gmail.com 
> > wrote:
> 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.
> Brad's point is fascinating - not least because I couldn't disagree  
> more. :)
> I do like the notion of understanding, but I suspect it is also  
> somewhat
> self-indulgent, because there may not be a level on which it can be  
> shared
> above that of working models.
> To help explain why, when I was working in astronomy there was a
> feeling among many of my colleagues that there should be a moratorium
> on publication of papers purporting to explain a particular classical
> phenomenon because the type of explanations being sought couldn't
> actually exist. The problem is a fairly basic bit of astrophysics -
> the transition of many stars from dwarfs to giants for the last tenth
> of their active lives. There is no mystery here: there are
> half a dozen equations and a bucket-full of well known physical data.
> You implement them on a computer and you get something that behaves  
> pretty
> much like a real star.  Then you've got your "prodable brain"
> equivalent and it is natural to seek higher level,
> intuitive, easily communicated, mathematically elegant explanations
> of what's going on. Quite a few (mutually incompatible) explanations
>  were published.
> The whole game unraveled however when people began addressing
> "what-if" questions with these models. By definition, the explanations
> are insensitive to quantitative details (like the opacity or
> pressure-density relationship for stellar material) but it turns out
> that if you compute what would happen with slightly different physics
> (bigger gravitational constant, different opacities etc) then stars
> don't necessarily turn into giants. So they are not actually  
> insensitive
> to the quantitative details. In effect the parameter
> space is lumpy and we're in a particular patch (of course, you can
> theorize about why we have to be in that patch but that's another
> question entirely). Elegant explanations assume smoothness but
> the space isn't smooth, so no such explanations can be correct.
> Another observation was that when you ask people to predict
> the outcomes of these what-if questions (about the only type of
> experimental intervention that is possible in astronomy) then the  
> people
> who write and run the programs often do better than the theorists.
> So, like most areas of expertise, you can develop an intuitive
> understanding and internal model of the domain by years of
> application, but there's no short cut - you can't get it from a
> book. Other people who want the same abilities will have to get
> there in the same way by internalizing the same mass of data.
> My point is that for this particular problem, high-level theory is
> not much use. Some of it is epiphenomenal, and the rest is just plain
> wrong. The models work fine but they are too complicated to run in
> your head. The simpler things that you can run in your head or on
> paper are too coarse to be any use.
> My personal guess, which I realize is deeply unpopular, is that this
> also applies to much of neuroscience. If we do have a simulated brain,
> then it will have been built using a vary large volume of data, a few
> equations and a lot of extremely sophisticated software
> engineering. I'm not sure there will be any point in looking for
> theories at a higher level than the design documents and software
> architecture that went into making it. If such complexity reduction
> were possible, then you'd hope the engineers have got there by then.
> The issue of whether, when you have a 64 bit floating point unit at
> your disposal instead of a mass of synapses and ion channels, you can
> make an equivalent but more mathematical and easily computed version
> of a purkinje cell seems like a case of premature optimization
> (or perhaps of engineering expediency) driven by todays prevalent
> technology, not a question of durable scientific relevance.
> As I see it, the area that needs the most attention, (both funding
> and education) but receives practically none, is not maths, but how on
> earth we develop the software engineering and data management
> concepts, languages and technologies that will enable us to build the
> next n generations of models.
> Robert
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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

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