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Numerical analyses and breadboard experiments of twin attractors in two-neuron-based non-autonomous Hopfield neural network

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Abstract

This paper investigates twin attractors in a two-neuron-based non-autonomous Hopfield neural network (HNN) through numerical analyses and hardware experiments. Stability analysis of the DC equilibrium point is executed and an unstable saddle-focus is found in the parameter region of interest. The stimulus-associated dynamical behaviors are numerically explored by bifurcation diagrams and dynamical map in two-dimensional parameter-space, from which coexisting twin attractors behavior can be observed with the variations of two stimulus-associated parameters. Moreover, breadboard experiment investigations are carried out, which effectively verify the numerical simulations.

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Authors and Affiliations

  1. School of Information Science and Engineering, Changzhou University, Changzhou, 213164, P.R. China

    Quan Xu, Zhe Song, Hui Qian, Mo Chen, Pingye Wu & Bocheng Bao

Authors
  1. Quan Xu
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  2. Zhe Song
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  3. Hui Qian
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  4. Mo Chen
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  5. Pingye Wu
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  6. Bocheng Bao
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Correspondence to Bocheng Bao.

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Xu, Q., Song, Z., Qian, H. et al. Numerical analyses and breadboard experiments of twin attractors in two-neuron-based non-autonomous Hopfield neural network. Eur. Phys. J. Spec. Top. 227, 777–786 (2018). https://doi.org/10.1140/epjst/e2018-700122-3

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  • DOI: https://doi.org/10.1140/epjst/e2018-700122-3

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