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A spatio-temporal learning rule based on the physiological data of LTP induction in the hippocampal CA1 network

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Artificial Neural Networks — ICANN 96 (ICANN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1112))

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Abstract

We studied the LTP inducing factors using temporally and spatially modulated stimuli given to the hippocampal neural network. It was found that when the spatial factors were maintained to be constant the positive correlation in the successive inter-stimulus intervals contributes to produce larger LTP. On the other hand, if the temporal factors are kept constant, the spatial coinsidence contributes to poroduce lager LTP. We propose a learning rule by which these experimental results can be consistently interpreted.

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Authors and Affiliations

  1. Department of Information & Communication Engineering Faculty of Engineering, Tamagawa University, 6-1-1, Tamagawagakuen, Machida, 194, Tokyo, Japan

    Minoru Tsukada, Takeshi Aihara & Hide-aki Saito

  2. Department of Physiology, Yamagata University, School of Medicine, Japan

    Hiroshi Kato

Authors
  1. Minoru Tsukada
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  2. Takeshi Aihara
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  3. Hide-aki Saito
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  4. Hiroshi Kato
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Editor information

Christoph von der Malsburg Werner von Seelen Jan C. Vorbrüggen Bernhard Sendhoff

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© 1996 Springer-Verlag Berlin Heidelberg

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Tsukada, M., Aihara, T., Saito, Ha., Kato, H. (1996). A spatio-temporal learning rule based on the physiological data of LTP induction in the hippocampal CA1 network. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_120

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  • DOI: https://doi.org/10.1007/3-540-61510-5_120

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61510-1

  • Online ISBN: 978-3-540-68684-2

  • eBook Packages: Springer Book Archive

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