Vibrational resonance in a randomly connected neural network

Qin, Yingmei; Che, Yanqiu; Zhao, Jia

doi:10.1007/s11571-018-9492-2

Cited by 23 publications

(10 citation statements)

References 49 publications

(59 reference statements)

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“…It was first identified and demonstrated numerically by Landa and McClintock [9], confirmed theoretically by Gitterman [10] and by Blekhman and Landa [11,12] and detected experimentally in vertical cavity surface emitting lasers and optical systems [13][14][15][16][17]. In VR, the response of a nonlinear system to the effect of the low-frequency (LF) component of the bi-harmonic signal can be amplified by the presence of the high-frequency (HF) component when the difference between the frequencies is sufficiently large ( [7,15,[18][19][20][21][22][23][24][25][26][27][28] and references therein). The VR scenario is analogous to stochastic resonance (SR) but with the high-frequency input force taking the place of noise [29,30].…”

Section: Introductionmentioning

confidence: 85%

Quantum vibrational resonance in a dual-frequency-driven Tietz-Hua quantum well

et al. 2020

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We investigate the response of a quantum particle in the Tietz-Hua quantum potential driven by biharmonc fields-a low and a very high-frequency force. The response is characterized by the occurrence of a maximum in the first-order transition probability amplitude, |s| 2 , under the influence of the applied fields. It is shown that in the absence of the high-frequency component of the applied fields, |s| 2 shows a distinct sequence of resonances; whereas an increase in the amplitude of the high-frequency field induces minima in |s| 2. However, the |s| 2 maximum occurs in the lowfrequency regime where it may be considered otherwise weak in the presence of a single harmonic force.

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Section: Introductionmentioning

confidence: 85%

Quantum vibrational resonance in a dual-frequency-driven Tietz-Hua quantum well

et al. 2020

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“…This phenomena is known as stochastic resonance (SR). Weak signals were also detected under sine-Wiener noise in the FitzHugh-Nagumo model (Yao and Ma 2018) and with high frequency inputs in a noisy neural network model (vibrational resonance) (Qin et al 2018).…”

Section: Introductionmentioning

confidence: 96%

Random pulse induced synchronization and resonance in uncoupled non-identical neuron models

Nakamura

Tateno

2019

Cogn Neurodyn

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Random pulses contribute to stochastic resonance in neuron models, whereas common random pulses cause stochasticsynchronized excitation in uncoupled neuron models. We studied concurrent phenomena contributing to phase synchronization and stochastic resonance following induction by a weak common random pulse in uncoupled non-identical Hodgkin-Huxley type neuron models. The common random pulse was selected from a gamma distribution and the degree of synchronization depended on the corresponding shape parameter. Specifically, a low shape parameter of the weak random pulse induced well-synchronized spiking in uncoupled neuron models, whereas a high shape parameter of the weak random pulse or a weak periodic pulse caused low degrees of synchronization. These were improved by concurrent inputs of periodic and random pulses with high shape parameters. Finally, the output pulse was synchronized with the periodic pulse, and the common random pulse revealed periodic responses in the present neuron models.

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“…However, most of the previous theoretical and numerical works on synchronization mainly based on the single neuron type in the neuronal networks. Due to the complexity of the brain, studying the dynamical behaviors for both cases of excitatory and inhibitory neurons is becoming more and more popular in complex neuronal networks [21], [21]- [24]. The authors have explored the interconnectivity between excitatory-inhibitory neural networks [23], [24], and they also analysed that the strength of inhibitory intra-connectivity plays an essential role in determining the dynamics of Pyramidal Interneuron Network Gamma (PING) mechanism.…”

Section: Introductionmentioning

confidence: 99%

“…Many different kinds of network structures are considered to investigate the synchronization and rhythm transition in the neuronal network [12], [13], [18]- [22], [33]- [35]. Qin have studied the vibrational resonance in a randomly network [21]. Kim investigated the burst synchronization in a scale-free neuronal network [20], [35].…”

Section: Introductionmentioning

confidence: 99%

Synchronization and Rhythm Transition in a Complex Neuronal Network

et al. 2020

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Synchronization and rhythm transition in an excitatory-inhibitory balanced cortical neuronal network are investigated in this paper. A small-world neuronal network is performed to be the cortical region of cerebral cortex, which is composed of different types of Izhikevich neurons. The combination of regular spiking (RS) cells, chattering (CH) cells, or mixed RS and CH cells are imitated as excitatory neurons, whereas fast spiking (FS) cells mimic inhibitory neurons. We mainly focus on the effect of different types of neurons on synchronization and rhythm transition of the neuronal network. The simulation results illustrate that it is easier for the neuronal network with CH excitatory neurons to achieve synchronization, and it is also more susceptible to the parameters than the network with RS neurons. Together with the weight of the synaptic connection, the structure of the small-world network is also explored to identify its influence on the synchronization and the rhythm transition. Moreover, we found that the synchronization performance could be improved by increasing the degree of influence among neurons. Importantly, the synchronization states are associated with the rhythm transitions. Especially, the consistency of synchronization of the neuronal network and γ band rhythm is illustrated. In addition, our results could have an important significance on further understanding brain rhythms and synchrony in neuroscience.

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Vibrational resonance in a randomly connected neural network

Cited by 23 publications

References 49 publications

Quantum vibrational resonance in a dual-frequency-driven Tietz-Hua quantum well

Quantum vibrational resonance in a dual-frequency-driven Tietz-Hua quantum well

Random pulse induced synchronization and resonance in uncoupled non-identical neuron models

Synchronization and Rhythm Transition in a Complex Neuronal Network

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