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Harris Georgiou wrote:

*> ----- Original Message -----
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*> From: Cristian Fabbi <fabbic@galactica.it>
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*> Newsgroups: comp.ai.fuzzy
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*> Sent: Tuesday, May 01, 2001 11:43 AM
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*> Subject: Humans think fuzzy?
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*>
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*> > Hi you all.
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*> > Soon I've read that neural system made with FL think like humans (i'm
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*> > summarizing).
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*> > Why do we say so? Have we got proofs that human brain works in a fuzzy
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*> way?
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*> > I'd like to start reflections about this point.
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*> > Thank you very much for the attention.
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*> >
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*> > Cristian Fabbi
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*> > Psychologist
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*> > www.fuzzylogic.f2s.com
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*> >
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*>
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*> Hello,
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*> It i strue that no solid proof have been established that humans think
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*> fuzzy, but it is a fact that human brain learns most of the things in a
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*> trial-and-error scheme. Artificial neurons (software) can simulate in much
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*> detail the basic function of biological neurons and so far we know that the
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*> brain has no binary "static" logic as computers do. A neuron output
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*> fluctuates between two boundary values (say, "0" and "1") but all the values
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*> in between are used to characterize an unknown or ambiguous input, something
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*> that works much like a fuzzy fuction, not binary for sure. Dr. R.Penrose has
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*> some interesting ideas how real human neurons work in a much more
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*> complicated way, using quantum physics properties in signal propagation, but
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*> the fact is that all reasoning and logical associations we normally may be
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*> traced down to simple fuzzy mappings.
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*> My point is, the question is not whether fuzziness is a real human brain
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*> proparty, but rather how realistic are the current fuzzy (or ANN) models -
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*> usually the goal is to approximate human's optimum for a specific function,
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*> not the real human brain itself (something that is far beyond our current
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*> and near future technological abilities).
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*>
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*> ___________________________
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*> Harris Georgiou
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*> Informatics Systems Analyst (MSc)
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*> mailto:xgeorgio@hol.gr
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*>
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*>
*

Hi everyone,

A neuron clearly showing the fuzzy logic INCLUSIVE OR property of passing the

maximum value among it inputs has been found in

the auditory system of the frog brain (see the home page of my web site

http://neurocomputing.org for the data and reference). Yet neural signals have

more than one parameter (dimension) unlike the values used in conventional logic

and probability. Neural signals have a pulse width (number of action potentials)

and within that pulse the action potentials have a frequency. In addition the

frequency has a variance and often a decay. Finally, a variable latency often

occurs before the pulse is triggered.

Certainly neural signals have a validity value in addition to a truth value to

make it at least a 2-dimensional multivalued logic (a generalization of fuzzy and

modal logic as pointed out by Stephen Lehmke in a posting to this newsgroup). But

how are these 2 dimensions used and defined? How do they interact? Is there a

relation to probability? And what about their variances?

Probability and logic and not independent. Consider this question about a car

belonging to the sample set of vehicles. "What is the probability that a car is

behind a wall?" The probability depends upon the precision of the definition for

"car". Does "car" include a pick-up truck, a sport utility vehicle, a van?

Sincerely,

David Olmsted

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