Path: utzoo!utgpu!watserv1!watmath!att!att!pacbell.com!ucsd!usc!ucla-cs!oahu.cs.ucla.edu!kirkaas
From: kirkaas@oahu.cs.ucla.edu (paul kirkaas)
Newsgroups: comp.ai.neural-nets
Subject: Alternative input/transfer functions for a neuron
Message-ID: <1990Nov29.023654.9491@cs.ucla.edu>
Date: 29 Nov 90 02:36:54 GMT
Sender: news@cs.ucla.edu (Mr. News)
Organization: UCLA Computer Science Department
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I am looking for some different types of input/transfer functions on
neural inputs.

Take a single neuron with a one dimensional input field.
I want to it classify two types of very simple input
patterns --- those with two peaks, and those with one.
It has plenty of inputs; starting with X1 at the left of the input field
proceeding to XN at the far right.  Each input is either 1 or 0 depending
on whether the signal is high or low.

I'm not concerned with learning; I will worry about that later.

 Examples:


Two Peaks
         ________________        __________________
________|                |______|                  |__

One Peak

         __________________________________________
________|                                          |__


Two Peaks
            __   __
___________|  |_|  |__________________________________


One Peak

            _______
___________|       |__________________________________
         

Two Peaks
                                         __   __
________________________________________|  |_|  |______

....


Now, it seems clear that the standard input function, the
linear weighted sum of inputs, SUM( X * W), will be unable to
divide the two input categories of 1 or 2 peaks.  What I am interested
in is some other smiple manipulation of input values that could make
that distinction --- in other words, some way to do scale and translation
invarience in a simple neuron with a simple input pattern.

Any suggestions or references?

Thanks.

Paul

-- 
Paul Kirkaas
kirkaas@cs.ucla.edu