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On adaptive parameters identification of Hindmarsh–Rose neuron models

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  • Kovalchukov, Aleksandr
  • Fradkov, Alexander

Abstract

This publication is devoted to the exploration of the Hindmarsh–Rose model, a biological neuron model that provides a good balance between complexity and variability. We focus on the model parameter identification problem, which is a critical aspect of control system theory. The complexity of the problem arises from the presence of numerous nonlinear functions and a large number of unknown parameters. The following sub-issues are covered in this work.

Suggested Citation

  • Kovalchukov, Aleksandr & Fradkov, Alexander, 2025. "On adaptive parameters identification of Hindmarsh–Rose neuron models," Chaos, Solitons & Fractals, Elsevier, vol. 200(P1).
    • Handle: RePEc:eee:chsofr:v:200:y:2025:i:p1:s0960077925008288
    DOI: 10.1016/j.chaos.2025.116815
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    References listed on IDEAS

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    1. Fernandez, Leandro E. & Carpio, Agustin & Wu, Jiaming & Boccaletti, Stefano & Rozenberg, Marcelo & Mindlin, Gabriel B., 2024. "A model for an electronic spiking neuron built with a memristive voltage-gated element," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    2. Semenov, Danila M. & Fradkov, Alexander L., 2021. "Adaptive synchronization in the complex heterogeneous networks of Hindmarsh–Rose neurons," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    3. Andreev, Andrey V. & Maksimenko, Vladimir A. & Pisarchik, Alexander N. & Hramov, Alexander E., 2021. "Synchronization of interacted spiking neuronal networks with inhibitory coupling," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
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