creators_name: Rinkus, Gerard J.
type: confpaper
datestamp: 2004-03-06
lastmod: 2011-03-11 08:55:29
metadata_visibility: show
title: TEMECOR: An Associative, Spatio-temporal Pattern Memory for Complex State Sequences
ispublished: pub
subjects: comp-neuro-sci
subjects: comp-sci-neural-nets
full_text_status: public
keywords: spatiotemporal pattern memory sequence associative
abstract: The problem of representing large sets of complex state sequences (CSSs)---i.e., sequences in which states can recur multiple times---has thus far resisted solution.  This paper describes a novel neural network model, TEMECOR, which has very large capacity for storing CSSs.  Furthermore, in contrast to the various back-propagation-based attempts at solving the CSS problem, TEMECOR requires only a single presentation of each sequence. TEMECOR's power derives from a) its use of a combinatorial, distributed representation scheme, and b) its method of choosing internal representations of states at random. Simulation results are presented which show that the
number of spatio-temporal binary feature patterns which can be stored to some criterion accuracy (e.g., 97%) increases faster-than-linearly in the size of the network.  This is true for both uncorrelated and correlated pattern sets,
although the rate is slightly slower for correlated patterns.
date: 1995
date_type: published
publisher: Lawrence Erlbaum Associates, Inc. and INNS Press
pagerange: 442-448
refereed: TRUE
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citation:   Rinkus, Gerard J.  (1995) TEMECOR: An Associative, Spatio-temporal Pattern Memory for Complex State Sequences.  [Conference Paper]     
document_url: http://cogprints.org/3472/1/wcnn95.pdf