**From:** my_sunshine (*sun@faclib-0119.unh.edu*)

**Date:** Thu May 17 2001 - 07:09:04 MDT

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This still seems (at least borderline) relevant to sl4...

First, let me say two things, then I'll answer your question...

*>neurons and their respective neural codes. I have never heard of
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*>Fourier computing though. And, I was until now unaware that there was a
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*>need to "invent the kind of component which the human brain uses." I
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*>guess I have been in the dark about something -- could you fill me in?
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(1) I did not mean to imply, by my message, that a brain-like computing

component was necessary to implement AI, but merely one option.

(2) I am not a neuroscientist and am not up on cutting-edge knowledge about

neural operations, but this is what I meant by "Fourier computing"...

(3) First, take a look at how the semantics of digital logic are mapped to

voltage levels:

Operands: binary voltages

# we map voltage intervals to boolean values...

(0.5 V <= v <= 1.5 V) -> boolean(v,1)

~(0.5 V <= v <= 1.5 V) -> boolean(v,0)

Operations: logic gates

# we map the domain (v0,v1), through a logical function, to a range (o).

boolean(v0,0) A boolean(v1,0) -> boolean(o,0) # logical OR

boolean(v0,0) A boolean(v1,1) -> boolean(o,1)

boolean(v0,1) A boolean(v1,0) -> boolean(o,1)

boolean(v0,1) A boolean(v1,1) -> boolean(o,1)

But, neurons don't work like this...

Let's take a first-order fourier decomposition of v:

v(t) = a1 cos w1*t + noise... and disregard the value a1

Remap operands:

(30/s <= w1 <= 40/s) -> boolean(v,1)

~(30/s <= w1 <= 40/s) -> boolean(v,0)

Remap operations:

We can symbolically expand any line (all lines) of the logic table:

i.e., boolean(v0,0) A boolean(v1,0) -> boolean(o,0) becomes

~(0.5 V <= v0 <= 1.5 V) A ~(0.5 V <= v1 <= 1.5 V) ->

~(0.5 V <= o <= 1.5 V)

and so on until the truth table has been expressed as a (complicated)

piecewise function o = logicalOr(v0,v1).

Likewise can we construct o = logicalOr(v0,v1) using the fourier

interperetations of v0, v1, and o. It would look something like:

~(30/s <= w0 <= 40/s) A ~(30/s <= w1 <= 40/s) ->

~(30/s <= o <= 40/s) ....

The piecewise function o = logicalOr(v0,v1) can thus be constructed

in which the fundamental frequency of o is a (logical) function of the

fundamental frequencies of v0 and v1.

This is the important part: Any variable in the fourier decomposition

of a function f(x) can be used to carry data, i.e.:

v(t) = a1 cos (w1*t+p1) + a2 cos (w2*t+p2) + ...

a1, a2, w1, w2, p1, and p2 can all be used to encode inputs and store

outputs. Note that, in this perspective, frequency, amplitude, and

phase can all be given significance. What we have done is employed a

different semantic mapping to v by transforing v(t) into the time

domain.

This is, in some cases, how the human nervous system puts neurons to

use. When sensing heat, for example, (using v = a1 cos w1) it is not

the value of a1, but the value w1, which conveys to the brain the

intensity of the heat. It is *more rapid*, not *more intense*,

neuron firing which indicates a sensation of greater heat. Clearly,

the brain has some way of semantically mapping such frequencies,

since I can tell hot from luke warm from scalding....

By constructing circuits which implement these mathematical functions

(ask any EE), electronics which operate in this manner can be used to

build entire computers. This is what I meant by "Fourier computing".

Dave

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