TEMECOR: An Associative, Spatio-temporal Pattern Memory for Complex State Sequences

Rinkus, Gerard J. (1995) TEMECOR: An Associative, Spatio-temporal Pattern Memory for Complex State Sequences. [Conference Paper]

Full text available as:



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.

Item Type:Conference Paper
Keywords:spatiotemporal pattern memory sequence associative
Subjects:Neuroscience > Computational Neuroscience
Computer Science > Neural Nets
ID Code:3472
Deposited By:Rinkus, Gerard J.
Deposited On:06 Mar 2004
Last Modified:11 Mar 2011 08:55

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

Cleeremans, A. (1993) Mechanisms of Implicit Learning: Connectionist Models of Sequence Processing. A Bradford Book, The MIT Press, Cambridge, MA.

Elman, J. L. (1990) “Finding Structure in Time” Cognitive Science, 14, 179-212.

Guyon, I., Personnaz, L., & Dreyfus, G. (1988) “Of points and loops” In Eckmiller, R. & Malsburg, C.v.d. (Eds.) Neural Computers, NATO ASI Series, Vol. F41, 261-269. Springer-Verlag, Berlin, Germany.

Jordan, M. I. (1986) “Serial Order” Tech. Rep. 8604, Institute for Cognitive Science, University of California, San Diego, CA.

McCloskey, M. & Cohen, N. J. (1989) “Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem”, In The Psychology of Learning and Memory Vol. 24. Bower, G. H. (Ed.) Academic Press. 109-165.

Rinkus, G. (1993) “Context-sensitive Spatio-temporal Memory” Tech. Rep. CAS/CNS-TR-93-031, Dept. of Cognitive and Neural Systems, Boston University, Boston, MA

Rinkus, G. (1995) A Combinatorial Neural Network Exhibiting both Episodic Memory and Generalization for Spatio-Temporal Patterns. Ph.D. Thesis, Graduate School of Arts and Sciences, Boston University. In Progress.

Smith, A. W. & Zipser, D. (1989) “Learning Sequential Structure with the Real-Time Recurrent Learning Algorithm” International Journal of Neural Systems, 1, 125-131.

Williams, R.J. & Zipser, D. (1989) “A learning algorithm for continually running fully recurrent neural networks” Neural Computation, 1, 270-280.


Repository Staff Only: item control page