Concomitance of inverse stochastic resonance and stochastic resonance in a minimal bistable spiking neural circuit

Zamani, AmirPasha; Novikov, Nikita A.; Gutkin, Boris

doi:10.1016/j.cnsns.2019.105024

Cited by 18 publications

(13 citation statements)

References 44 publications

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“…The analysis presented in [48] may, therefore, provide a qualitative explanation for this particular behavior. Zamani et al [51] later showed a similar behavior between SR and ISR in a minimal bistable spiking neural circuit, where both mechanisms could co-exist under careful preparations of the neural circuit. Whether and if so, how all of these different types of noise-induced resonance mechanisms are related and what efficient enhancement schemes for information processing emerge from their interactions is far from being completely understood.…”

Section: Introductionmentioning

confidence: 90%

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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Section: Introductionmentioning

confidence: 90%

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

Yamakou

Tran²,

Jost³

2022

Front. Phys.

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“…Stochastic resonance (SR) is a noise-induced phenomenon reported in dynamical systems with a threshold, while reverse stochastic resonance (ISR) is the opposite phenomenon that occurs in dynamical systems with bistability between a stable node and a stable limit cycle. In [13] the possible coexistence of both phenomena, SR and ISR, is shown in a minimal circuit of neurons that are synaptically coupled. Simulations indicate that the main characteristics of SR are the direct results of dynamic properties that lead to ISR.…”

Section: Coupled Neuron Dynamicsmentioning

confidence: 99%

Excitable dynamics in neural and cardiac systems

Barrio

Coombes

Desroches

et al. 2020

Communications in Nonlinear Science and Numerical Simulation

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The application of mathematics, physics and engineering to medical research is continuously growing; interactions among these disciplines have become increasingly important and have contributed to an improved understanding of clinical and biological phenomena, with implications for disease prevention, diagnosis and treatment. This special issue presents examples of this synergy, with a particular focus on the investigation of cardiac and neural excitability.This issue includes 24 original research papers and covers a broad range of topics related to the physiological and pathophysiological function of the brain and the heart. Studies span scales from isolated neurons and small networks of neurons to whole-organ dynamics for the brain and from cardiac subcellular domains and cardiomyocytes to onedimensional tissues for the heart.This preface is part of the Special Issue on "Excitable Dynamics in Neural and Cardiac Systems".

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“…( 2005 ) and Zamani et al. ( 2020 ). These include, amongst others, the well-known stochastic resonance (SR) (Longtin 1993 ; Wiesenfeld and Moss 1995 ; Lindner et al.…”

Section: Introductionmentioning

confidence: 96%

Control of noise-induced coherent oscillations in three-neuron motifs

et al. 2021

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The phenomenon of self-induced stochastic resonance (SISR) requires a nontrivial scaling limit between the deterministic and the stochastic timescales of an excitable system, leading to the emergence of coherent oscillations which are absent without noise. In this paper, we numerically investigate SISR and its control in single neurons and three-neuron motifs made up of the Morris–Lecar model. In single neurons, we compare the effects of electrical and chemical autapses on the degree of coherence of the oscillations due to SISR. In the motifs, we compare the effects of altering the synaptic time-delayed couplings and the topologies on the degree of SISR. Finally, we provide two enhancement strategies for a particularly poor degree of SISR in motifs with chemical synapses: (1) we show that a poor SISR can be significantly enhanced by attaching an electrical or an excitatory chemical autapse on one of the neurons, and (2) we show that by multiplexing the motif with a poor SISR to another motif (with a high SISR in isolation), the degree of SISR in the former motif can be significantly enhanced. We show that the efficiency of these enhancement strategies depends on the topology of the motifs and the nature of synaptic time-delayed couplings mediating the multiplexing connections.

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Concomitance of inverse stochastic resonance and stochastic resonance in a minimal bistable spiking neural circuit

Cited by 18 publications

References 44 publications

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

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

Excitable dynamics in neural and cardiac systems

Control of noise-induced coherent oscillations in three-neuron motifs

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