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International Conference on Artificial Neural Networks

ICANN 2013: Artificial Neural Networks and Machine Learning – ICANN 2013 pp 264–271Cite as

Memory Trace in Spiking Neural Networks

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 8131)

Abstract

Spiking neural networks have a limited memory capacity, such that a stimulus arriving at time t would vanish over a timescale of 200-300 milliseconds [1]. Therefore, only neural computations that require history dependencies within this short range can be accomplished. In this paper, the limited memory capacity of a spiking neural network is extended by coupling it to an delayed-dynamical system. This presents the possibility of information exchange between spiking neurons and continuous delayed systems.

Keywords

  • spiking neural networks
  • memory trace
  • delayed-dynamical systems
  • reservoir computing

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Author information

Authors and Affiliations

  1. Institute of Cognitive Sciences, University of Osnabrueck, Germany

    Marta Castellano & Gordon Pipa

Authors
  1. Marta Castellano
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  2. Gordon Pipa
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Editor information

Editors and Affiliations

  1. Faculty Automation,, Technical University of Sofia, 8 St. Kl. Ohridski Blvd., 1000, Sofia, Bulgaria

    Valeri Mladenov

  2. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev str. bl.25A, 1113, Sofia, Bulgaria

    Petia Koprinkova-Hristova

  3. Institute of Neural Information Processing, University of Ulm, 89075, Ulm, Germany

    Günther Palm

  4. Quartier UNIL-Dorigny, Bâtiment Internef, Université de Lausanne, 1015, Lausanne, Switzerland

    Alessandro E. P. Villa

  5. Department of Computer Science, University of Milano, Via Comelico, 39, 20135, Milano, Italy

    Bruno Appollini

  6. Knowledge Engineering, School of Computing and Mathematical Sciences, Auckland University of Technology, 120 Mayoral Drive, 3rd floor, 1010, Auckland, New Zealand

    Nikola Kasabov

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© 2013 Springer-Verlag Berlin Heidelberg

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Castellano, M., Pipa, G. (2013). Memory Trace in Spiking Neural Networks. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_33

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  • DOI: https://doi.org/10.1007/978-3-642-40728-4_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40727-7

  • Online ISBN: 978-3-642-40728-4

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