Coherence resonance in stimulated neuronal network

Андреев, А. В.; Макаров, В. В.; Runnova, Anastasiya E.; Pisarchik, Alexander N.; Hramov, Alexander E.

doi:10.1016/j.chaos.2017.11.017

Cited by 67 publications

(29 citation statements)

References 23 publications

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“…The manipulation of chemical and electrical patterns in the brain has become more accessible, either via drugs that cross the blood brain barrier, via electrical stimulation delivered through electrodes implanted in the brain, or via light delivered through optical fibers selectively exciting genetically manipulated neurons (Runnova et al, 2016 ; De Domenico, 2017 ; Andreev et al, 2018 ); however, the manipulation of the functional connectivity seems to be a more difficult goal to achieve. Our approach of modeling multi-layer networks in combination with stochastic dynamics offers a novel perspective on the modeling of the brain's structural and functional connectivity.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, multiplex networks of neurons can be formed from different network layers depending on their connectivity through a chemical link or by an ionic channel. In brain networks, different regions can be seen connected by functional and structural neural networks (Pisarchik et al, 2014 ; De Domenico, 2017 ; Andreev et al, 2018 ). In a multiplex network, each type of interaction between the nodes is described by a single layer network and the different layers of networks describe the different modes of interaction.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Self-Induced Stochastic Resonance in Multiplex Neural Networks: Electrical vs. Chemical Synapses

Yamakou

Hjorth

Martens

2020

Front. Comput. Neurosci.

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Electrical and chemical synapses shape the dynamics of neural networks, and their functional roles in information processing have been a longstanding question in neurobiology. In this paper, we investigate the role of synapses on the optimization of the phenomenon of self-induced stochastic resonance in a delayed multiplex neural network by using analytical and numerical methods. We consider a two-layer multiplex network in which, at the intra-layer level, neurons are coupled either by electrical synapses or by inhibitory chemical synapses. For each isolated layer, computations indicate that weaker electrical and chemical synaptic couplings are better optimizers of self-induced stochastic resonance. In addition, regardless of the synaptic strengths, shorter electrical synaptic delays are found to be better optimizers of the phenomenon than shorter chemical synaptic delays, while longer chemical synaptic delays are better optimizers than longer electrical synaptic delays; in both cases, the poorer optimizers are, in fact, worst. It is found that electrical, inhibitory, or excitatory chemical multiplexing of the two layers having only electrical synapses at the intra-layer levels can each optimize the phenomenon. Additionally, only excitatory chemical multiplexing of the two layers having only inhibitory chemical synapses at the intra-layer levels can optimize the phenomenon. These results may guide experiments aimed at establishing or confirming to the mechanism of self-induced stochastic resonance in networks of artificial neural circuits as well as in real biological neural networks.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Self-Induced Stochastic Resonance in Multiplex Neural Networks: Electrical vs. Chemical Synapses

Yamakou

Hjorth

Martens

2020

Front. Comput. Neurosci.

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“…In this respect, we suppose that the improvement of neuronal communication can be achieved through coherence resonance (CR) which appears in the neuronal network at a certain level of noise. CR in neuronal networks was predicted by numerical simulations of different neural models 13–15 . CR in the brain implies the existence of intrinsic brain noise caused by internal thermal fluctuations of membrane conductance mediated by random opening and closing ion channels 16,17 .…”

Section: Introductionmentioning

confidence: 99%

Coherent resonance in the distributed cortical network during sensory information processing

Pisarchik

Maksimenko

Андреев

et al. 2019

Sci Rep

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Neuronal brain network is a distributed computing system, whose architecture is dynamically adjusted to provide optimal performance of sensory processing. A small amount of visual information needed effortlessly be processed, activates neural activity in occipital and parietal areas. Conversely, a visual task which requires sustained attention to process a large amount of sensory information, involves a set of long-distance connections between parietal and frontal areas coordinating the activity of these distant brain regions. We demonstrate that while neural interactions result in coherence, the strongest connection is achieved through coherence resonance induced by adjusting intrinsic brain noise.

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“…19,20 These phenomena are actively studied in neuron models. [21][22][23][24][25][26] Qualitative transformations and ecological shifts caused by random influences, similar to phase transitions, were also found in population systems. 6,7,[27][28][29][30][31][32] Population dynamics belong to the wider area of investigation on the dynamics of complex ecological systems.…”

Section: Introductionmentioning

confidence: 82%

Analysis of ecological shifts in the two‐age structured population model with Allee effect and environmental noise

Bashkirtseva

Valenti

Spagnolo

2020

Math Methods in App Sciences

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We study noise-induced transformations in the two-age structured population model with the Allee effect and environmental fluctuations. In this bistable model, ecological shifts are associated mathematically with random transitions between basins of coexisting attractors. The following phenomena are revealed and studied: (i) noise-induced extinction, (ii) stochastic regeneration, and (iii) excitement of random mixed-mode oscillations. Constructive abilities of the analytical method of confidence domains are demonstrated in the parametric study of these phenomena.

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Coherence resonance in stimulated neuronal network

Cited by 67 publications

References 23 publications

Optimal Self-Induced Stochastic Resonance in Multiplex Neural Networks: Electrical vs. Chemical Synapses

Optimal Self-Induced Stochastic Resonance in Multiplex Neural Networks: Electrical vs. Chemical Synapses

Coherent resonance in the distributed cortical network during sensory information processing

Analysis of ecological shifts in the two‐age structured population model with Allee effect and environmental noise

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