paper avail.: "TEMECOR: An Associative, Episodic, Temporal Sequence Memory"

[rinkus@PARK.BU.EDU]

FTP-host: cns-ftp.bu.edu
FTP-file: pub/rinkus/nips95_rinkus.ps.Z

The following paper, which has been submitted to NIPS-95, and which is an
extended and revised version of a paper that has been accepted for invited
address at WCNN-95, is available via anonymous FTP at the above location.
The paper is 8 pages long.

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          TEMECOR: An Associative, Episodic, Temporal Sequence Memory

                             Gerard J. Rinkus
               Cognitive and Neural Systems Department
                            Boston University
                            Boston, MA 02215
                            rinkus at cns.bu.edu


                                 ABSTRACT

A distributed associative neural model of {\em episodic memory}\/ for
spatio-temporal patterns is presented.  The model exhibits {\em
faster-than-linear}\/ capacity scaling, under single-trial learning, for both
uncorrelated and correlated patterns.   The correlated pattern sets used in
simulations reported herein are formally, sets of {\em complex state
sequences}\/ (CSSs)---i.e. sequences in which states can recur multiple
times. Efficient representation of large sets of CSSs is central to speech and
language processing.  The English lexicon, for example, is formally
representable as a set of many thousands of CSSs over an alphabet of about 50
phonemes.  The model chooses internal representations (IRs) for each state in
a highly random fashion.  This implies maximal {\em dispersion}---i.e. maximal
average Hamming distance---over the set of IRs chosen during learning. Maximal
dispersion yields maximal episodic availability of the traces of the
individual exemplars.


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