Non-Gaussian noise optimized spiking activity of Hodgkin–Huxley neurons on random complex networks

Gong, Yubing; Hao, Yinghang; Xie, Yanhang; Ma, Xiao‐Guang

doi:10.1016/j.bpc.2009.07.001

Cited by 24 publications

(7 citation statements)

References 52 publications

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“…Our previous study has shown that for the networks with constant coupling strength the CR by the non-Gaussian noise occurs only when p < 0.15 [39]. While, for the present networks with TPCS, the CR by the non-Gaussain noise can occur in networks with larger randomness even up to p = 0.3.…”

Section: Resultscontrasting

confidence: 50%

Periodic Coupling Strength-Enhanced Coherence Resonance Induced by a Particular Kind of Non-Gaussian Noise in Neuronal Networks

Gong

Wang

2012

Fluct. Noise Lett.

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Communicated by Natalia B. JansonIn this Letter, we study the effect of time-periodic coupling strength (TPCS) on the coherence resonance (CR) of spiking behavior induced by a particular kind of nonGaussian noise in Newman-Watts networks of Hodgkin-Huxley neurons. It is found that the CR by the non-Gaussian noise can be enhanced by TPCS when TPCS frequency is equal to or multiple of the inverse of the refractory period, and can occur in networks with more random shortcuts for TPCS than for constant coupling strength. Furthermore, the CR by the non-Gaussian noise can occur at smaller TPCS frequency when network randomness increases. These results show that the CR by the non-Gaussian noise can be enhanced by TPCS and can occur in more complex networks in case of TPCS. These findings may help to better understand the joint roles of the non-Gaussian noise and TPCS in the spiking activity of the neuronal networks.

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Section: Resultscontrasting

confidence: 50%

Periodic Coupling Strength-Enhanced Coherence Resonance Induced by a Particular Kind of Non-Gaussian Noise in Neuronal Networks

Gong

Wang

2012

Fluct. Noise Lett.

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“…The range of correlation rates simulated, 0.005-5 ms -1 , encompasses the typical rates found in studies using the Hodgkin-Huxley model with colored noise, which are on the order of 0.5 ms -1 (Lee and Kim 1999; Yu et al 2001; Lee et al 1998) for transmembrane noise, 0.09 and 0.37 ms 1 for conductance-based noise models (Wenning et al 2005; Wenning and Obermayer 2003) and 1 ms -1 (Gong et al 2009; Y. H. Hao et al 2011) or 1.6-3.3 ms -1 (Sun and Lu 2014) for network analysis.…”

Section: Resultsmentioning

confidence: 54%

A neuron model of stochastic resonance using rectangular pulse trains

Danziger

Grill

2014

J Comput Neurosci

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Stochastic resonance (SR) is the enhanced representation of a weak input signal by the addition of an optimal level of broadband noise to a nonlinear (threshold) system. Since its discovery in the 1980s the domain of input signals shown to be applicable to SR has greatly expanded, from strictly periodic inputs to now nearly any aperiodic forcing function. The perturbations (noise) used to generate SR have also expanded, from white noise to now colored noise or vibrational forcing. This study demonstrates that a new class of perturbations can achieve SR, namely, series of stochastically generated biphasic pulse trains. Using these pulse trains as ‘noise’ we show that a Hodgkin Huxley model neuron exhibits SR behavior when detecting weak input signals. This result is of particular interest to neuroscience because nearly all artificial neural stimulation is implemented with square current or voltage pulses rather than broadband noise, and this new method may facilitate the translation of the performance gains achievable through SR to neural prosthetics.

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“…Over the last two decades, much attention has been paid to the nature and the effect of a particular kind of non-Gaussian colored noise (NGN) on the kinetics of various nonlinear dynamical systems [30][31][32][33][34][35][36][37][38]. Very recently, the NGN-induced spiking CR and synchronization in Hodgkin-Huxley (HH) neurons [39][40][41] and CR of stochastic rate oscillations in NO reduction with CO on small-size Pt(100) surfaces have been found [42].…”

Section: Introductionmentioning

confidence: 98%

Non-Gaussian noise-enhanced stochastic rate oscillations in CO oxidation on nanometer-sized Pd particles

2010

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The effect of non-Gaussian colored noise (NGN), mainly its departure q from the Gaussian noise, on the optimal ISRO of CO oxidation on nanometer-sized Pd particles was studied. It was found that the ISRO in the absence of external noise can still be enhanced when the NGN is applied. Specifically, the ISRO varies with changing q and becomes more regular at an appropriate q value, and when q is optimal, the ISRO becomes the most regular. Because the departure q from the Gaussian noise determines the probability distribution function and hence may denote the types of noise, this result shows that different types of external noise can enhance the ISRO of CO oxidation, and non-Gaussian noise may enhance the ISRO more greatly than the Gaussian noise. Therefore, non-Gaussian noise could play more effective roles in the catalytic process of CO oxidation on nanometer-sized Pd particles.non-Gaussian colored noise, catalytic CO oxidation on nanometer Pd particles, intrinsic stochastic rate oscillation, coherence resonance

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Non-Gaussian noise optimized spiking activity of Hodgkin–Huxley neurons on random complex networks

Cited by 24 publications

References 52 publications

Periodic Coupling Strength-Enhanced Coherence Resonance Induced by a Particular Kind of Non-Gaussian Noise in Neuronal Networks

Periodic Coupling Strength-Enhanced Coherence Resonance Induced by a Particular Kind of Non-Gaussian Noise in Neuronal Networks

A neuron model of stochastic resonance using rectangular pulse trains

Non-Gaussian noise-enhanced stochastic rate oscillations in CO oxidation on nanometer-sized Pd particles

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