Detection of sub-threshold periodic signal by multiplicative and additive cross-correlated sine-Wiener noises in the FitzHugh–Nagumo neuron

Yao, Yuangen; Ma, Chengzhang; Wang, Can-Jun; Yi, Ming; Gui, Rong

doi:10.1016/j.physa.2017.11.052

Cited by 39 publications

(8 citation statements)

References 43 publications

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“…Noise [32], highfrequency signals [33], chaotic signals [34] can enhance neuronal response to weak signals. The phenomena of information transmission and detection have been studied in neural networks such as chain networks [35], smallworld networks [36], feedforward networks [37].…”

Section: Introductionmentioning

confidence: 99%

Filtering properties of Hodgkin–Huxley neuron on different time-scale signals

Wang²,

Li³

et al. 2023

Communications in Nonlinear Science and Numerical Simulation

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Section: Introductionmentioning

confidence: 99%

Filtering properties of Hodgkin–Huxley neuron on different time-scale signals

Wang²,

Li³

et al. 2023

Communications in Nonlinear Science and Numerical Simulation

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Section: Introductionmentioning

confidence: 99%

Filtering Properties of Hodgkin-Huxley Neuron to Different Time-Scale Signals

Dong

Wang

et al. 2021

Preprint

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Neuron can be excited and inhibited by filtered signals. The filtering properties of neural networks have a huge impact on memory, learning, and disease. In this paper, the filtering properties of Hodgkin-Huxley neuron to different time-scale signals are investigated. It is found that the neuronal filtering property depends on the locking relationship between the signal's frequency band and the natural frequency of neuron. The natural firing frequency is a combination of the fundamental component and the various level harmonic components. The response of neuron to the filtered signal is related to the amplitude of the harmonic components. Neuron responds better to the low-frequency signals than the high-frequency signals because of the reduction in the harmonic component amplitude. The filtering ability of neuron can be modulated by the excitation level, and is stronger around the excitation threshold. Our results might provide novel insights into the filtering properties of neural networks and guide the construction of artificial neural networks.

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“…42 Cross-correlated sine-Wiener (CCSW) bounded noises can induce coherence resonance, 43 improve sub-threshold periodic detection in the FHN neuron. 44 Moreover, CCSW bounded noises can also result in breakup of spiral wave and order-disorder spatial pattern transition. 45 In consideration of the importance of synchronization in brain functions, here we investigate the synchronization and synchronization transition in a small-world neuronal network of delayed FHN neurons subjected to SW bounded noise.…”

Section: Introductionmentioning

confidence: 99%

Delay-induced synchronization transition in a small-world neuronal network of FitzHugh–Nagumo neurons subjected to sine-Wiener bounded noise

Yao

Hou

2019

Int. J. Mod. Phys. B

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Noise and delay are ubiquitous in brain and they have significant effects on neuronal network synchronization and even brain functions. Based on a small-world neuronal network of delayed FitzHugh–Nagumo (FHN) neurons subjected to sine-Wiener (SW) bounded noise, the effects of delay and SW noise on synchronization and synchronization transition are numerically investigated by calculating a synchronization measure R and plotting spatiotemporal patterns. The phenomenon of delay-induced synchronization transition is observed as delay [Formula: see text] is increased. And large self-correlation time and strength of SW noise can increase the number of delay-induced synchronization transition. In addition, delay-induced synchronization transition is robust against the change of topology structure of neuronal network and this phenomenon becomes much easier to see for small nearest neighbors k in the small-world network. Since synchronization transition may imply functional switch, our results may have important implications, and inspire future studies.

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Detection of sub-threshold periodic signal by multiplicative and additive cross-correlated sine-Wiener noises in the FitzHugh–Nagumo neuron

Cited by 39 publications

References 43 publications

Filtering properties of Hodgkin–Huxley neuron on different time-scale signals

Filtering properties of Hodgkin–Huxley neuron on different time-scale signals

Filtering Properties of Hodgkin-Huxley Neuron to Different Time-Scale Signals

Delay-induced synchronization transition in a small-world neuronal network of FitzHugh–Nagumo neurons subjected to sine-Wiener bounded noise

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