title: TEMECOR: An Associative, Spatio-temporal Pattern Memory for Complex State Sequences
creator: Rinkus, Gerard J.
subject: Computational Neuroscience
subject: Neural Nets
description: 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.
publisher: Lawrence Erlbaum Associates, Inc. and INNS Press
date: 1995
type: Conference Paper
type: PeerReviewed
format: application/pdf
identifier: http://cogprints.org/3472/1/wcnn95.pdf
identifier:   Rinkus, Gerard J.  (1995) TEMECOR: An Associative, Spatio-temporal Pattern Memory for Complex State Sequences.  [Conference Paper]     
relation: http://cogprints.org/3472/