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

J. Bose; S. B. Furber; J. L. Shapiro

doi:10.1007/11550822_19

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

J. Bose, S. B. Furber, J. L. Shapiro

Advanced Processor Technology

Research output: Chapter in Book/Conference proceeding › Conference contribution

Abstract

In this paper we present a neural sequence machine that can learn temporal sequences of discrete symbols, and perform better than machines that use Elman's context layer, time delay nets or shift register-like context memories. This machine can perform sequence detection, prediction and learning of new sequences. The network model is an associative memory with a separate store for the sequence context of a pattern. Learning is one-shot. The model is capable of both off-line and on-line learning. The machine is based upon a sparse distributed memory which is used to store associations between the current context and the input symbol. Numerical tests have been done on the machine to verify its properties. We have also shown that it is possible to implement the memory using spiking neurons. © Springer-Verlag Berlin Heidelberg 2005.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)\|Lect. Notes Comput. Sci.
Publisher	Springer Nature
Pages	115-120
Number of pages	5
Volume	3696
ISBN (Print)	3540287523, 9783540287520
DOIs	https://doi.org/10.1007/11550822_19
Publication status	Published - 2005
Event	15th International Conference on Artificial Neural Networks: Biological Inspirations - ICANN 2005 - Warsaw Duration: 1 Jul 2005 → … http://dblp.uni-trier.de/db/conf/icann/icann2005-1.html#BoseFS05http://dblp.uni-trier.de/rec/bibtex/conf/icann/BoseFS05.xmlhttp://dblp.uni-trier.de/rec/bibtex/conf/icann/BoseFS05

Publication series

Name	Lecture Notes in Computer Science

Conference

Conference	15th International Conference on Artificial Neural Networks: Biological Inspirations - ICANN 2005
City	Warsaw
Period	1/07/05 → …
Internet address	http://dblp.uni-trier.de/db/conf/icann/icann2005-1.html#BoseFS05http://dblp.uni-trier.de/rec/bibtex/conf/icann/BoseFS05.xmlhttp://dblp.uni-trier.de/rec/bibtex/conf/icann/BoseFS05

Access to Document

10.1007/11550822_19

Cite this

Bose, J., Furber, S. B., & Shapiro, J. L. (2005). A spiking neural sparse distributed memory implementation for learning and predicting temporal sequences. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci. (Vol. 3696, pp. 115-120). (Lecture Notes in Computer Science). Springer Nature. https://doi.org/10.1007/11550822_19

Bose, J. ; Furber, S. B. ; Shapiro, J. L. / 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)|Lect. Notes Comput. Sci.. Vol. 3696 Springer Nature, 2005. pp. 115-120 (Lecture Notes in Computer Science).

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abstract = "In this paper we present a neural sequence machine that can learn temporal sequences of discrete symbols, and perform better than machines that use Elman's context layer, time delay nets or shift register-like context memories. This machine can perform sequence detection, prediction and learning of new sequences. The network model is an associative memory with a separate store for the sequence context of a pattern. Learning is one-shot. The model is capable of both off-line and on-line learning. The machine is based upon a sparse distributed memory which is used to store associations between the current context and the input symbol. Numerical tests have been done on the machine to verify its properties. We have also shown that it is possible to implement the memory using spiking neurons. {\textcopyright} Springer-Verlag Berlin Heidelberg 2005.",

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Bose, J, Furber, SB & Shapiro, JL 2005, A spiking neural sparse distributed memory implementation for learning and predicting temporal sequences. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.. vol. 3696, Lecture Notes in Computer Science, Springer Nature, pp. 115-120, 15th International Conference on Artificial Neural Networks: Biological Inspirations - ICANN 2005, Warsaw, 1/07/05. https://doi.org/10.1007/11550822_19

A spiking neural sparse distributed memory implementation for learning and predicting temporal sequences. / Bose, J.; Furber, S. B.; Shapiro, J. L.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.. Vol. 3696 Springer Nature, 2005. p. 115-120 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Conference proceeding › Conference contribution

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AB - In this paper we present a neural sequence machine that can learn temporal sequences of discrete symbols, and perform better than machines that use Elman's context layer, time delay nets or shift register-like context memories. This machine can perform sequence detection, prediction and learning of new sequences. The network model is an associative memory with a separate store for the sequence context of a pattern. Learning is one-shot. The model is capable of both off-line and on-line learning. The machine is based upon a sparse distributed memory which is used to store associations between the current context and the input symbol. Numerical tests have been done on the machine to verify its properties. We have also shown that it is possible to implement the memory using spiking neurons. © Springer-Verlag Berlin Heidelberg 2005.

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Bose J, Furber SB , Shapiro JL. A spiking neural sparse distributed memory implementation for learning and predicting temporal sequences. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.. Vol. 3696. Springer Nature. 2005. p. 115-120. (Lecture Notes in Computer Science). doi: 10.1007/11550822_19

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

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