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From: vlsi_lib@netcom.com (Gerard Malecki)
Subject: Re: Thought Question
Message-ID: <vlsi_libD2oKoJ.GKw@netcom.com>
Organization: VLSI Libraries Incorporated
References: <1995Jan12.022935.26572@Princeton.EDU> <3fmba3$nmf@vixen.cso.uiuc.edu> <3fmn70$cpa@pentagon.io.com>
Date: Fri, 20 Jan 1995 01:41:07 GMT
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Xref: glinda.oz.cs.cmu.edu comp.ai.alife:1866 comp.ai.philosophy:24814 comp.ai:26644

In article <3fmn70$cpa@pentagon.io.com> larrys@pentagon.io.com (Larry Smith) writes:
>
>Neurons have been simulated by a computer, which _is_ a Turing machine.

This does not mean that neurons are themselves Turing machines. A neuron
has a cretain degree of randomness to it, which may depend on quantum
phenomena. A main characterisitic feature of Turing machines is repeatability
which is not the case with real-life neurons. In fact, a lot of processes
such as thermal noise in a resistor are of true random nature that cannot be
classified to be Turing machines.

>Which strongly suggests that a sufficiently butch computer could simulate
>a whole _lot_ of neurons, which could be networked together the same way
>as a brain.  I don't think it is considered _proved_ that the human
>brain is a Turing machine, but it is extremely strongly implied by all
>current research.

I'm not buying that. In a real-life digital computer, quantum effects 
such as thermal noise have no effect on the course of the execution since
they are completely masked by the relatively large noise margin between
the two logic states. In the human brain which is mostly analog, noise
in the neurons would have a cumulative effect via the so called butterfly
effect. If we run two simulations of the brain with the same initial 
state and input stimuli but with different sequences of random numbers
to model the neural noise, the two outcomes would diverge in no time.
Whether the variations are behaviorally significant is a moot point.

Shankar Ramakrishnan
shankar@vlibs.com


