TR EE Short Term Memory - Hysterisis

[Manoel F Tenorio]

Subject: TR-EE 90-63: The Hystery Unit - short term memory
Bcc: tenorio
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The task of performing recognition of patterns on spatio-temporal signals is
not an easy one, primarily due to the time structure of the signal. Classical
methods of handling this problem have proven themselves unsatisfactory, and
they range from "projecting out" the time axis, to "memorizing" the entire
sequence before a decision can be made. In particular, the latter can be
very difficult if no a priori information about signal length is present,
if the signal can suffer compression and extension, or if the entire pattern
is massively large, as in the case of time varying imagery. 

Neural Network models to solve this problem have either been based on the 
classical approach or on recursive loops within the network which can make
learning algorithms numerically unstable.

It is clear that for all the spatio-temporal processing,  done 
by biological systems,  some kind of short term memory is needed, 
and has been long conjectured. In this report, we have
taken the first step at the design of a spatio-temporal system  that deals
naturally with the problems present in this type of processing. In particular
we investigate the exchange of the simple sigmoid function, commonly used, by
a hysterisis function. Later, with the addition of an integrator which 
represents the neuron membrane effect, we construct a simple computational
device to perform spatio-pattern recognition tasks.

The results are that for bipolar input sequence, this device remaps the entire
sequence into a real number. Knowing the output of the device suffices for
knowing the sequence. For trajectories embbeded in noise, the device shows
superior recognition to other techniques. Furthermore, properties of the
device allows the designer to determine the memory length, and explain
with simple circuits sensitization and habituation phenomena. The report
below deals with the device and its mathematical properties. Other
forthcoming papers will concentrate on other aspects of circuits constructed
with this device.

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Requests from within US, Canada, and Mexico:

    The technical report with figures has been/will soon be
placed in the account kindly provided by Ohio State.  Here
is the instruction to get the files:

        ftp cheops.cis.ohio-state.edu    (or, ftp 128.146.8.62)
        Name: anonymous
        Password: neuron
        ftp> cd pub/neuroprose
        ftp> mget tom.hystery*    (type y and hit return)
        ftp> quit
        unix> uncompress tom.hystery*.Z
        unix> lpr -P(your_postscript_printer) tom.hystery.ps
        unix> lpr -P(your_Mac_laserwriter) tom.hystery_figs.ps

Please contact mdtom at ecn.purdue.edu for technical difficulties.

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Requests from outside North America:

    The technical report is available at a cost of US$22.39 per copy,
postage included.  Please make checks payable to Purdue University
in US dollars.  You may send your requests, checks, and full first
class mail address to:

        J. L. Dixon
        School of Electrical Engineering
        Purdue University
        West Lafayette, Indiana 47907
        USA

Please mention the technical report number: TR-EE 90-63.

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          The Hystery Unit - A Short Term Memory Model
                    for Computational Neurons

                          M. Daniel Tom
                     Manoel Fernando Tenorio

           Parallel Distributed Structures Laboratory
                School of Electrical Engineering
                        Purdue University
               West Lafayette, Indiana  47907, USA

                         December, 1990

Abstract: In  this  paper,  a  model  of  short  term  memory  is
introduced.   This model is inspired by the transient behavior of
neurons and magnetic storage as memory.  The  transient  response
of  a  neuron  is  hypothesized  to be a combination of a pair of
sigmoids, and a relation is drawn to the hysteresis loop found in
magnetic  materials.   A model is created as a composition of two
coupled families of curves.  Two theorems are  derived  regarding
the  asymptotic  convergence  behavior  of  the  model.   Another
conjecture claims that the model retains full memory of all  past
unit step inputs.