[Comp-neuro] Re: Attractors, variability and noise
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.
On Aug 13, 2008, at 1:46 PM, Brad Wyble wrote:
> At the risk of missing my flight I can't resist continuing this
> 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.
> 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
> 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.
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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