Re: The brain is like a surface - a hypersurface - a maximum hypersurface.
- From: vkarlamov@xxxxxxxxx
- Date: 20 Jun 2005 16:32:30 -0700
Mindpixel wrote:
> I learned from John McCrone in 2003 of a scene that took place in the
> early 90s between the theoretical neurobiologist, Karl Friston and
> Harvard Psychologist, Stephen Kosslyn as they stood by a pond at London
> Zoo.
>
> "Look," said Friston, "Traditional thinking holds that the brain is
> some kind of computer, crunching its way through billions of inputs
> each second to output a state of consciousness. But really, the brain
> acts more as if the arrival of imput provokes a widespread disturbance
> in some already existing state."
>
> Friston nodded down at the pond, "That gives you a better way of
> thinking about it. The brain is like a surface, its circuts drawn tight
> in a certain state of tension. You toss in a pebble - that´s your
> sensory input - and you immediately get ripples of activity. Sure the
> patterns say something about the way the pebble hit the surface, but
> they are mixed with the lingering patterns of earlier pebbles of input.
> And then everything begins echoing off the sides of the pond. The
> overall shape of the system has an effect on the patterns you see.
> Nothing is being calculated. The response of the system evolves
> organically. Or to use the proper term, dynamically."
>
> "And as we throw more pebbles - or rather - experiences - into this
> particular pond, we change its shape, and thus the kinds of patterns it
> tends to produce. This is a system that learns," agreed Kosslyn. "It
> has a memory!"
>
> To discover that top brain scientists were thinking geometrically gave
> me a very nice feeling. Yes, they could see how the metaphor of a
> surface and of shape were far closer to the realty of the brain than a
> computer. I knew I was going to have a much easier time convincing the
> world to take seriously the idea that evolution maximized hypersurface
> and that the mind and brain are unified in shape in seven dimensions,
> than I had expected.
>
Would very-large-scale neural nets be a good model of this?
.
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