2004-03-06Z2011-03-11T08:55:29Zhttp://cogprints.org/id/eprint/3472This item is in the repository with the URL: http://cogprints.org/id/eprint/34722004-03-06ZTEMECOR: An Associative, Spatio-temporal Pattern Memory for Complex State SequencesThe 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.Gerard J. Rinkus