Control of coherence resonance in multiplex neural networks

Masoliver, Maria; Masoller, Cristina; Zakharova, Anna

doi:10.1016/j.chaos.2021.110666

Cited by 28 publications

(10 citation statements)

References 44 publications

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“…In neuronal dynamics, oscillations represent the neuronal spikes or bursts and the system represents a neuron or a network. CR has been observed in neural networks such as globally coupled networks [17] , [18] , [19] , [20] , [21] , [22] , randomly connected neural networks exhibiting single oscillations [23] , [24] , small-world networks [25] , [26] , ring networks [27] , [28] , multiplex networks [29] , [30] , [31] , and the influencer network of phase oscillators [32] . However, the stochastic dynamics of a calcium-mediated random and sparse heterogeneous bursting network have not been extensively investigated and the effect of CR in such a network remains elusive.…”

Section: Introductionmentioning

confidence: 99%

Noise-induced network bursts and coherence in a calcium-mediated neural network

Jagdev

Morgovsky

2021

Heliyon

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Noise-induced population bursting has been widely identified to play important roles in information processes. We construct a mathematical model for a random and sparse heterogeneous neural network where bursting can be induced from a resting state by a global stochastic stimulus. Importantly, the noise-induced bursting dynamics of this network are mediated by calcium conductance. We use two spectral measures to evaluate network coherence in the context of the network bursts, the spike trains of all neurons, and the individual bursts of all neurons. Our results show that the coherence of the network is optimized by an optimal level of the stochastic stimulus, which is known as coherence resonance (CR). We also demonstrate that the interplay of the calcium conductance and noise intensity can modify the degree of CR.

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Section: Introductionmentioning

confidence: 99%

Noise-induced network bursts and coherence in a calcium-mediated neural network

Jagdev

Morgovsky

2021

Heliyon

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“…The characteristic features of CR and SISR based on (1) timedelayed feedback couplings and network topology [1,15,30], 2) the multiplexing of layer networks [6,31,39,46,50], and 3) the use of one type of noise-induced resonance mechanism to optimize another type [50] have been established. It has been shown that appropriate selection of the time-delayed feedback parameters of FHN neurons coupled in a ring network can modulate CR: with a local coupling topology, synaptic time delay weakens CR, while in cases of non-local and global coupling, only appropriate synaptic time delays can strengthen or weaken CR [1,15,30,38].…”

Section: Introductionmentioning

confidence: 99%

“…Enhancement schemes based on multiplexing (or, in general, on connecting several layers to form a multilayer network) are advantageous because the dynamics of one layer can be controlled by adjusting the parameters of another layer. So far, the enhancement of CR and SISR based on the multiplexing of neural layer networks has been established only in regular networks in the absence of STDP [6,31,39,46,50].…”

Section: Introductionmentioning

confidence: 99%

Optimal Resonances in Multiplex Neural Networks Driven by an STDP Learning Rule

Yamakou

Tran²,

Jost³

2022

Front. Phys.

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In this paper, we numerically investigate two distinct phenomena, coherence resonance (CR) and self-induced stochastic resonance (SISR), in multiplex neural networks in the presence of spike-timing-dependent plasticity (STDP). The high degree of CR achieved in one layer network turns out to be more robust than that of SISR against variations in the network topology and the STDP parameters. This behavior is the opposite of the one presented by Yamakou and Jost (Phys. Rev. E 100, 022313, 2019), where SISR is more robust than CR against variations in the network parameters but in the absence of STDP. Moreover, the degree of SISR in one layer network increases with a decreasing (increasing) depression temporal window (potentiation adjusting rate) of STDP. However, the poor degree of SISR in one layer network can be significantly enhanced by multiplexing this layer with another one exhibiting a high degree of CR or SISR and suitable inter-layer STDP parameter values. In addition, for all inter-layer STDP parameter values, the enhancement strategy of SISR based on the occurrence of SISR outperforms the one based on CR. Finally, the optimal enhancement strategy of SISR based on the occurrence of SISR (CR) occurs via long-term potentiation (long-term depression) of the inter-layer synaptic weights.

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“…Moreover, the coherence resonance and self-induced stochastic resonance in a multiplex neuronal network can be controlled by the network topology as well as by the intra- and interlayer time-delayed couplings (Yamakou and Jost, 2019 ; Yamakou et al, 2020 ). In a specific scenario where only one layer displays noise-induced spiking activity, a weak coupling between two neuronal populations in a multiplexed configuration was found to lead to coherence, anticoherence, and inverse stochastic resonances, as specified by the characteristics of interlayer links (Masoliver et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Interlayer Connectivity Affects the Coherence Resonance and Population Activity Patterns in Two-Layered Networks of Excitatory and Inhibitory Neurons

Ristič

Gosak

2022

Front. Comput. Neurosci.

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The firing patterns of neuronal populations often exhibit emergent collective oscillations, which can display substantial regularity even though the dynamics of individual elements is very stochastic. One of the many phenomena that is often studied in this context is coherence resonance, where additional noise leads to improved regularity of spiking activity in neurons. In this work, we investigate how the coherence resonance phenomenon manifests itself in populations of excitatory and inhibitory neurons. In our simulations, we use the coupled FitzHugh-Nagumo oscillators in the excitable regime and in the presence of neuronal noise. Formally, our model is based on the concept of a two-layered network, where one layer contains inhibitory neurons, the other excitatory neurons, and the interlayer connections represent heterotypic interactions. The neuronal activity is simulated in realistic coupling schemes in which neurons within each layer are connected with undirected connections, whereas neurons of different types are connected with directed interlayer connections. In this setting, we investigate how different neurophysiological determinants affect the coherence resonance. Specifically, we focus on the proportion of inhibitory neurons, the proportion of excitatory interlayer axons, and the architecture of interlayer connections between inhibitory and excitatory neurons. Our results reveal that the regularity of simulated neural activity can be increased by a stronger damping of the excitatory layer. This can be accomplished with a higher proportion of inhibitory neurons, a higher fraction of inhibitory interlayer axons, a stronger coupling between inhibitory axons, or by a heterogeneous configuration of interlayer connections. Our approach of modeling multilayered neuronal networks in combination with stochastic dynamics offers a novel perspective on how the neural architecture can affect neural information processing and provide possible applications in designing networks of artificial neural circuits to optimize their function via noise-induced phenomena.

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Control of coherence resonance in multiplex neural networks

Cited by 28 publications

References 44 publications

Noise-induced network bursts and coherence in a calcium-mediated neural network

Noise-induced network bursts and coherence in a calcium-mediated neural network

Optimal Resonances in Multiplex Neural Networks Driven by an STDP Learning Rule

Interlayer Connectivity Affects the Coherence Resonance and Population Activity Patterns in Two-Layered Networks of Excitatory and Inhibitory Neurons

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