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An evolutionary approach to associative memory in recurrent neural networks

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Abstract

In this paper, we investigate the associative memory in recurrent neural networks, based on the model of evolving neural networks proposed by Nolfi, Miglino and Parisi.Experimentally developed network has highly asymmetric synaptic weights and dilute connections, quite different from those of the Hopfield model.Some results on the effect of learning efficiency on the evolution are also presented.

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Authors and Affiliations

  1. Graduate School of Science and Technology, Kobe University Rokkodai, Nada, 657, Kobe, Japan

    Shigetaka Fujita

  2. Department of Information Science, Hyogo University of Education, Yashiro-cho, Kato-gun, 673-14, Hyogo, Japan

    Haruhiko Nishimura

Authors
  1. Shigetaka Fujita
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  2. Haruhiko Nishimura
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Fujita, S., Nishimura, H. An evolutionary approach to associative memory in recurrent neural networks. Neural Process Lett 1, 9–13 (1994). https://doi.org/10.1007/BF02310936

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