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Combined effects of spike-timing-dependent plasticity and homeostatic structural plasticity on coherence resonance

Marius E. Yamakou1,2,* and Christian Kuehn3,4,†

  • 1Department of Data Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 11, 91058 Erlangen, Germany
  • 2Max-Planck-Institut für Mathematik in den Naturwissenschaften, Inselstr. 22, 04103 Leipzig, Germany
  • 3Faculty of Mathematics, Technical University of Munich, Boltzmannstrasse 3, 85748 Garching bei München, Germany
  • 4Complexity Science Hub Vienna, Josefstädter Strasse 39, 1080 Vienna, Austria

  • *marius.yamakou@fau.de
  • †ckuehn@ma.tum.de
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Phys. Rev. E 107, 044302 – Published 13 April, 2023

DOI: https://doi.org/10.1103/PhysRevE.107.044302

Abstract

Efficient processing and transfer of information in neurons have been linked to noise-induced resonance phenomena such as coherence resonance (CR), and adaptive rules in neural networks have been mostly linked to two prevalent mechanisms: spike-timing-dependent plasticity (STDP) and homeostatic structural plasticity (HSP). Thus this paper investigates CR in small-world and random adaptive networks of Hodgkin-Huxley neurons driven by STDP and HSP. Our numerical study indicates that the degree of CR strongly depends, and in different ways, on the adjusting rate parameter P, which controls STDP, on the characteristic rewiring frequency parameter F, which controls HSP, and on the parameters of the network topology. In particular, we found two robust behaviors. (i) Decreasing P (which enhances the weakening effect of STDP on synaptic weights) and decreasing F (which slows down the swapping rate of synapses between neurons) always leads to higher degrees of CR in small-world and random networks, provided that the synaptic time delay parameter τc has some appropriate values. (ii) Increasing the synaptic time delay τc induces multiple CR (MCR)—the occurrence of multiple peaks in the degree of coherence as τc changes—in small-world and random networks, with MCR becoming more pronounced at smaller values of P and F. Our results imply that STDP and HSP can jointly play an essential role in enhancing the time precision of firing necessary for optimal information processing and transfer in neural systems and could thus have applications in designing networks of noisy artificial neural circuits engineered to use CR to optimize information processing and transfer.

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Phys. Rev. E 107, 044302– Published 13 April, 2023
  • Received 14 November 2022
  • Revised 10 February 2023
  • Accepted 23 March 2023
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Open access publication funded by the Max Planck Society.

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