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@misc{cogprints3472, title = {TEMECOR: An Associative, Spatio-temporal Pattern Memory for Complex State Sequences}, author = {Gerard J. Rinkus}, publisher = {Lawrence Erlbaum Associates, Inc. and INNS Press}, year = {1995}, pages = {442--448}, keywords = {spatiotemporal pattern memory sequence associative}, url = {http://cogprints.org/3472/}, 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.} }