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Stimulus classification using chimera-like states in a spiking neural network

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  • Andreev, Andrey V.
  • Ivanchenko, Mikhail V.
  • Pisarchik, Alexander N.
  • Hramov, Alexander E.

Abstract

A complex network of bistable Hodgkin-Huxley (HH) neurons with excitatory coupling can exhibit a partially spiking chimera behavior. We propose to use this chimera-like state for classification of the entering stimulus amplitude in the neural network with coexisting resting and spiking states. Due to different additive noise applied to each neuron in the network, the neurons are nonidentical. Therefore, depending on the amplitude of the external current, a part of the neurons stays in the resting state, while another part oscillates. Keeping fixed the coupling strength between neurons inside the network, we train the neural network on external pulses with two different amplitudes to adjust the coupling strength between the network neurons and two output neurons. We consider two variants of the classifier, in the presence and in the absence of inhibitory coupling between output neurons, and study how the output neurons respond to the external pulses of different amplitudes. The accuracy of the proposed classifier reaches 100% when the output neurons are inhibitory coupled, so that only one of these neurons is activated.

Suggested Citation

  • Andreev, Andrey V. & Ivanchenko, Mikhail V. & Pisarchik, Alexander N. & Hramov, Alexander E., 2020. "Stimulus classification using chimera-like states in a spiking neural network," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    • Handle: RePEc:eee:chsofr:v:139:y:2020:i:c:s0960077920304586
    DOI: 10.1016/j.chaos.2020.110061
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    References listed on IDEAS

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    1. Runnova, Anastasiya E. & Hramov, Alexander E. & Grubov, Vadim V. & Koronovskii, Alexey A. & Kurovskaya, Maria K. & Pisarchik, Alexander N., 2016. "Theoretical background and experimental measurements of human brain noise intensity in perception of ambiguous images," Chaos, Solitons & Fractals, Elsevier, vol. 93(C), pages 201-206.
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    3. Nefeli Dimitra Tsigkri-DeSmedt & Johanne Hizanidis & Eckehard Schöll & Philipp Hövel & Astero Provata, 2017. "Chimeras in leaky integrate-and-fire neural networks: effects of reflecting connectivities," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 90(7), pages 1-11, July.
    4. Bandyopadhyay, Abhirup & Kar, Samarjit, 2018. "Impact of network structure on synchronization of Hindmarsh–Rose neurons coupled in structured network," Applied Mathematics and Computation, Elsevier, vol. 333(C), pages 194-212.
    5. Vladimir A. Maksimenko & Semen A. Kurkin & Elena N. Pitsik & Vyacheslav Yu. Musatov & Anastasia E. Runnova & Tatyana Yu. Efremova & Alexander E. Hramov & Alexander N. Pisarchik, 2018. "Artificial Neural Network Classification of Motor-Related EEG: An Increase in Classification Accuracy by Reducing Signal Complexity," Complexity, Hindawi, vol. 2018, pages 1-10, August.
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    Cited by:

    1. Li, Xuening & Xie, Ying & Ye, Zhiqiu & Huang, Weifang & Yang, Lijian & Zhan, Xuan & Jia, Ya, 2024. "Chimera-like state in the bistable excitatory-inhibitory cortical neuronal network," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    2. Guo, Lei & Guo, Minxin & Wu, Youxi & Xu, Guizhi, 2023. "Specific neural coding of fMRI spiking neural network based on time coding," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    3. Klinshov, Vladimir V. & Kovalchuk, Andrey V. & Franović, Igor & Perc, Matjaž & Svetec, Milan, 2022. "Rate chaos and memory lifetime in spiking neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    4. Rajagopal, Karthikeyan & Karthikeyan, Anitha, 2022. "Spiral waves and their characterization through spatioperiod and spatioenergy under distinct excitable media," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).

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