@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.}
}

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