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Dynamics of a Recurrent Spiking Neural Network in the Two-Alternative Choice Task

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Radiophysics and Quantum Electronics Aims and scope

We have revealed the dynamic mechanism of solving a cognitive task of two-alternative choice by an artificial recurrent network of spiking neurons. The approach to designing a functional network model is described based on machine learning methods. The formation of a modular coupling structure during training is established. The properties of the network response, which underlie the performing of a target task, are found.

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Authors and Affiliations

  1. Institute of Applied Physics of the Russian Academy of Sciences, Nizhny Novgorod, Russia

    M. M. Pugavko, O.V. Maslennikov & V. I. Nekorkin

  2. N. I. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia

    M. M. Pugavko, O.V. Maslennikov & V. I. Nekorkin

Authors
  1. M. M. Pugavko
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  2. O.V. Maslennikov
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  3. V. I. Nekorkin
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Corresponding author

Correspondence to O.V. Maslennikov.

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Translated from Izvestiya Vysshikh Uchebnykh Zavedenii, Radiofizika, Vol. 64, No. 10, pp. 817–832, October 2021. Russian DOI: https://doi.org/10.52452/00213462_2021_64_10_817

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Pugavko, M.M., Maslennikov, O. & Nekorkin, V.I. Dynamics of a Recurrent Spiking Neural Network in the Two-Alternative Choice Task. Radiophys Quantum El 64, 736–749 (2022). https://doi.org/10.1007/s11141-022-10175-2

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  • DOI: https://doi.org/10.1007/s11141-022-10175-2

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