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

Brad Wyble bwyble at gmail.com
Wed Aug 13 20:46:54 CEST 2008


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
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