A spiking neural sparse distributed memory implementation for learning and predicting temporal sequences

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3696 LNCS, Issue , 2005, Pages 115-120

  Bose, J ^a   Furber, S B ^a   Shapiro, J L ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CODES (SYMBOLS); MATHEMATICAL MODELS; NEURAL NETWORKS; PATTERN RECOGNITION; SIGNAL FILTERING AND PREDICTION; STORAGE ALLOCATION (COMPUTER);

DISCRETE SYMBOLS; SEQUENCE CONTEXT; TEMPORAL SEQUENCES;

LEARNING SYSTEMS;

EID: 33646189427     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11550822_19     Document Type: Conference Paper

References (8)

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3. P. Kanerva. Sparse Distributed Memory. MIT Press, 1988
4. J. L. Elman. Finding structure in time. Cognitive Science, 1990, 14.
5. S.B. Furber, J.M. Cumpstey, W.J. Bainbridge and S. Temple. Sparse distributed memory using N-of-M codes. Neural Networks. 2004, 10
6. K.J. Lang and G. E. Hinton. The development of the time delay neural network architecture for speech recognition. Tech.Report 88152, Carnegie Mellon, 1988.
7. R. Sun and C.L. Giles (Eds.) Sequence Learning. Springer-Verlag, 2000
8. W. Maass and C.M. Bishop (eds.) Pulsed Neural Networks MIT Press, 1998