Inverse stochastic resonance in electroconvection by multiplicative colored noise

Huh, Jong–Hoon

doi:10.1103/physreve.94.052702

Cited by 27 publications

(28 citation statements)

References 42 publications

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“…Furthermore, ISR might also play a critical role in computational mechanisms that require reduced firing activity without chemical inhibitory neuromodulation or, alternatively, when other computational mechanisms require on-off bursts of activity. On the other hand, apart from neural systems, a recent experimental study reported the existence of ISR in nematic liquid crystals (NLC) [23]. This work shows that a proper arrangement of colored noise intensity and its correlation time constant induces ISR in an ac-driven electroconvection in NLC.…”

Section: Introductionmentioning

confidence: 81%

A theoretical description of inverse stochastic resonance in nature

Torres

Uzuntarla

Marro

2020

Communications in Nonlinear Science and Numerical Simulation

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The inverse stochastic resonance (ISR) phenomenon consists in an unexpected depression in the response of a system under external noise, e.g., as observed in the behavior of the mean-firing rate in some pacemaker neurons in the presence of moderate values of noise. A possible requirement for such behavior is the existence of a bistable regime in the behavior of these neurons. We here explore theoretically the possible emergence of this behavior in a general bistable system, and conclude on conditions the potential function which drives the dynamics must accomplish. We show that such an intriguing, and apparently widely observed, phenomenon ensues in the case of an asymmetric potential function when the high activity minimum state of the system is metastable with the largest basin of attraction and the low activity state is the global minimum with a smaller basin of attraction. We discuss on the relevance of such a picture to understand the ISR features and to predict its general appearance in other natural systems that share the requirements described here. Finally, we report another intriguing non-standard stochastic resonance in our system, which occurs in the absence of any weak signal input into the system and whose emergence can be explained, with the ISR, within our theoretical framework in this paper in terms of the shape of the potential function.

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Section: Introductionmentioning

confidence: 81%

A theoretical description of inverse stochastic resonance in nature

Torres

Uzuntarla

Marro

2020

Communications in Nonlinear Science and Numerical Simulation

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“…In contrast, an opposing phenomenon known as the inverse stochastic resonance (ISR) was initially discovered in a neural system [14][15][16], showing a minimal output performance peak at a moderate optimal noise intensity. Later, ISR was also reported in other systems such as ecological systems and during electroconvection (EC) [17,18].…”

Section: Introductionmentioning

confidence: 90%

“…( 1) is additive, i.e., independent of the variable x [21,22]. Similarly, multiplicative noise (t) (i.e., x(t)) can provide SR [22][23][24] and ISR [17].…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the commonly used amplitude noise, our method consisted in using the, usually overlooked, phase noise [25,26]. Moreover, we used a colored noise with a finite autocorrelation time (c ≠ 0) [17,21,22,[27][28][29][30][31], instead of the conventional quasi-white noise (c ≈ 0) [2][3][4][5][6][7][8][9][10][11][12][13]27].…”

Section: Introductionmentioning

confidence: 99%

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Control of stochastic and inverse stochastic resonances in a liquid-crystal electroconvection system using amplitude and phase noises

Huh

Shiomi

Miyagawa

2023

Preprint

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Stochastic and inverse stochastic resonances are counterintuitive phenomena, in which noise plays a pivotal role in the dynamics of various biological and engineering systems. Even though these resonances have been identified in various systems, a transition between them has never been observed before. The present study demonstrates the presence of both resonances in a liquid crystal electroconvection system using combined amplitude and phase noises, which correspond to colored noises with appropriate cutoff frequencies (i.e., finite correlation times). We established the emergence of both resonances and their transition through systematic control of the electroconvection threshold voltage using these two noise sources. Our numerical simulations were experimentally confirmed and revealed how the output performance of the system could be controlled by combining the intensity and cutoff frequency of the two noises. Furthermore, we suggested the crucial contribution of a usually overlooked additional phase noise to the advancements in various noise-related fields.

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“…Noise is ubiquitous in the neural system, caused by thermal effects, activity of ion channels and etc. [13,14]. The appropriate noise plays an important role in information transmission and detection, which can induce stochastic resonance (SR), and optimizing the detection and propagation of weak signals [15][16][17][18].…”

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confidence: 99%

Influence of Temperature and Noise on Subthreshold Signal Propagation in Feedforward Neural Network

Dai

Liu

et al. 2021

Preprint

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Temperature is an important environmental factor that all creatures depend on. Under the appropriate temperature, the neural system shows good biological performance. Based on an improved Hodgkin-Huxley (HH) neuron model considering temperature and noise, the ten-layers pure excitatory feedforward neural network and the ten-layers excitatory-inhibitory (EI) neural network are constructed to study the subthreshold signal propagation. It’s found that increasing temperature can restrain the signal propagation, and raise the noises intensity threshold where the failed signal propagation can transform into succeed signal propagation. Under the large noise, the signal propagation in network in different temperatures exhibits different anti-noise capabilities. There exists the saturation value of interlayer connection probability, that is, the signal propagation maintains constant when interlayer connection probability beyond a certain value. Moreover, in EI network with large noise, the network’s intrinsic oscillation activity will completely cover subthreshold signal, and block the signal propagation. The jumping phenomenon in the value of fidelity, which measures the similarity between input signal and output signal, appears in both pure excitatory network and EI network. This paper provides potential value for understanding the regulation of both temperature and noise in information propagation in neural network.

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Inverse stochastic resonance in electroconvection by multiplicative colored noise

Cited by 27 publications

References 42 publications

A theoretical description of inverse stochastic resonance in nature

A theoretical description of inverse stochastic resonance in nature

Control of stochastic and inverse stochastic resonances in a liquid-crystal electroconvection system using amplitude and phase noises

Influence of Temperature and Noise on Subthreshold Signal Propagation in Feedforward Neural Network

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