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Synaptic compensation on Hopfield network: implications for memory rehabilitation

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Abstract

The discrete-time neural network proposed by Hopfield can be used for storing and recognizing binary patterns. Here, we investigate how the performance of this network on pattern recognition task is altered when neurons are removed and the weights of the synapses corresponding to these deleted neurons are divided among the remaining synapses. Five distinct ways of distributing such weights are evaluated. We speculate how this numerical work about synaptic compensation may help to guide experimental studies on memory rehabilitation interventions.

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Acknowledgments

LHAM is partially supported by the Brazilian National Council for Scientific and Technological Development (CNPq).

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Authors and Affiliations

  1. Escola de Engenharia, Pós-graduação em Engenharia Elétrica, Universidade Presbiteriana Mackenzie, Rua da Consolação, n.896, São Paulo, SP, 01302-907, Brazil

    R. A. Menezes & L. H. A. Monteiro

  2. Escola Politécnica, Departamento de Engenharia de Telecomunicações e Controle, Universidade de São Paulo, Av. Prof. Luciano Gualberto, travessa 3, n.380, São Paulo, SP, 05508-900, Brazil

    L. H. A. Monteiro

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  1. R. A. Menezes
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  2. L. H. A. Monteiro
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Correspondence to L. H. A. Monteiro.

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Menezes, R.A., Monteiro, L.H.A. Synaptic compensation on Hopfield network: implications for memory rehabilitation. Neural Comput & Applic 20, 753–757 (2011). https://doi.org/10.1007/s00521-010-0480-7

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  • DOI: https://doi.org/10.1007/s00521-010-0480-7

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