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

Brad Wyble bwyble at gmail.com
Wed Aug 13 17:45:12 CEST 2008


On Wed, Aug 13, 2008 at 11:16 AM, james bower <bower at uthscsa.edu> wrote:

> Obviously, the question of what we can and can't understand as individuals,
> or groups of individuals is somewhere near the core of this discussion.
>  Many would claim, as Brad does here, that of necessity our descriptions of
> things (brains) can not exceed the ability of humans to communicate what
> they understand.
>

That's precisely my point, well stated.


The point is this, high tech manufacturing already operates in the domain
> where no one individual, or even group of individuals understands the
> totality of the software systems they have built to make airplanes, chips,
> etc.  Yet these software systems are at the very heart of ever more
> sophisticated machines (although certainly vastly less sophisticated than
> the brain).  In Physics, it has been said that Gottried Leibniz (1646-1716)
> was the last human who knew everything.  So why in neuroscience is
> "communicability" a factor used to argue for abstract models, or levels of
> description that ignore neurons.   What happens if using GENESIS or NEURON
> or both with middle ware like NeuroConstruct, we collectively contribute to
> building a virtual representation of the real brain - that any one of us can
> use to poke around in something we find interesting.  Perhaps (although as I
> have said I doubt it), some Newton of the present or future might be able to
> come up with more general principles that apply to the structure or function
> of brain regions - or brains themselves -- but to start this process with a
> mandate for general principles to begin with - makes no sense to me.
>

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
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