On Tue, Jun 13, 2006 at 04:38:49PM +0100, Russell Wallace wrote:

> >Representing and manipulating formal system is a very recent component
> >in the fitness function, and hence not well-optimized.
> 
> True; but I will claim that no matter how much you optimize a biological
> neural net, it will always have characteristics such as being slow at serial
> computation, and relatively imprecise.

No disagreement. But you don't have to use live cells to build
a computational network. As to imprecise, with scaling down geometry
and ramping up switching speed digital is no longer well-defined.
With many small switches you're also getting reliability problems,
so noise begins to creep in at the hardware layer.
 
> Fast serial calculation. 

In comparison to biological neurons, yes.

> Very high precision. Extreme flexibility in choice

I don't see why an automaton network can't use many bits
to represent things. There's also some question for what
you need very high precision. Cryptography is a candidate,
another one is physical modelling doing it like a 
mathematician. I think there is a very distinct
bias, almost an agnosia if you want to do it not like
a mathematician.

> of operations and instant rewiring of data structures.

You can't actually rewire the circuit, so you have
to switch state which reprsents the circuit. It's easier
if you embrace the model of dynamically traced out
circuitry in a computational substrate. Very few
things are instant in a current memory-bottlenecked
digital computers. If you want to widen that bottleneck,
you first get a massively parallel box, and eventually
a cellular/mosaic architecture of simple computational
elements.
 
> If computational resources,
> >which architecture?
> 
> I don't understand the question, please clarify?

If you want to build a robot capable of playing tennis
in a heavy hail, how would you do it?

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Eugen* Leitl <a href="http://leitl.org";>leitl</a> http://leitl.org
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