Coherence resonance and stochastic synchronization in a small-world neural network: an interplay in the presence of spike-timing-dependent plasticity

Yamakou, Marius E.; Inack, Estelle M.

doi:10.1007/s11071-023-08238-8

Cited by 11 publications

(1 citation statement)

References 65 publications

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“…Noise is an inherent part of many complex systems and has been observed to induce a wide variety of phenomena [1][2][3][4][5]. In the case of neural systems, noise-induced resonance phenomena, such as stochastic resonance [3,[6][7][8], self-induced stochastic resonance (SISR) [9][10][11][12], inverse stochastic resonance [12][13][14][15] and coherence resonance (CR) [16][17][18][19], have been shown to play important functional roles in information processing, including amongst others, the detection of weak input signals in neural networks [20], and the local optimal information transfer between the input and output spike in neurons [14].…”

Section: Introductionmentioning

confidence: 99%

Combined effects of STDP and homeostatic structural plasticity on coherence resonance

Yamakou¹,

Kuehn²

2023

Preprint

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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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Section: Introductionmentioning

confidence: 99%

Combined effects of STDP and homeostatic structural plasticity on coherence resonance

Yamakou¹,

Kuehn²

2023

Preprint

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Robustness of consensus in double‐layer degree positive and negative correlation ring networks

Du,

Zhu,

Gao

et al. 2024

Math Methods in App Sciences

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The robustness of consensus in multilayer networks is affected by the network structure, especially the interlayer coupling approach. The article investigates the impact of two interlayer coupling approaches on the robustness of consensus in double‐layer ring networks, where the interlayer relationships are denoted as degree positive correlation (PC) and degree negative correlation (NC), respectively. The robustness of network consensus can be derived from network coherence. Two types of network coherence are first described using the Kirchhoff index and the number of spanning trees, providing a new calculation method for network coherence. The number of network nodes and the coupling strength have been shown to impact the coherence of the two types of networks. Combined with numerical simulation experiments, the NC model shows better consensus than the PC model under the same parameter set. In particular, the parameter values when the network consensus is optimal are obtained.

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A biophysical neuron model with double membranes

Li,

Ma,

Xie

2024

Nonlinear Dyn

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Coherence resonance and stochastic synchronization in a small-world neural network: an interplay in the presence of spike-timing-dependent plasticity

Cited by 11 publications

References 65 publications

Combined effects of STDP and homeostatic structural plasticity on coherence resonance

Combined effects of STDP and homeostatic structural plasticity on coherence resonance

Robustness of consensus in double‐layer degree positive and negative correlation ring networks

A biophysical neuron model with double membranes

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