TEMCOR: An Associative, Spatio-Temporal Pattern Memory for Complex State Sequences

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 patttern sets, although the rate is slightly slower for correlated patterns.