Ordering spatiotemporal chaos in complex thermosensitive neuron networks

In this paper, we numerically study the effect of ion channel noise on the synchronization of delayed Newman-Watts network of stochastic Hodgkin-Huxley neurons. It is found that time delay can induce synchronization transitions only when channel noise intensity is intermediate, and the synchronization transitions become most obvious when channel noise intensity is optimal. As channel noise intensity is varied, the neurons can also exhibit synchronization transitions, and the phenomenon is enhanced when time delay is tuned at around time delays that can induce synchronization transitions. Moreover, this phenomenon becomes most obvious when the value of coupling strength or the value of network randomness is optimal. These results show that channel noise has a subtle regulation effect on the synchronization of the neuronal network. These findings may find potential implications for the normal and pathological functions in the brain and the information processing and transmission in neural systems.

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“…The synchronization of the neuronal network is characterized by standard deviation σ defined as [50]:…”

Section: Model and Equationsmentioning

confidence: 99%

Effects of channel noise on synchronization transitions in Newman–Watts neuronal network with time delays

Wang

Gong

2015

Nonlinear Dyn

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“…It has been reported that increasing the network randomness may lead to an enhancement of temporal coherence and spatial synchronization. Spatiotemporal chaos and synchronization on complex neuronal networks have also been studied (Gong et al 2006;Wei and Luo 2007). Both works report that the synchronization, which is absent in the regular network, can be greatly enhanced by random shortcuts between distant neurons.…”

Section: Introductionmentioning

confidence: 99%

Oscillations and synchrony in a cortical neural network

Wang

Chen

et al. 2013

Cogn Neurodyn

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In this paper, the oscillations and synchronization status of two different network connectivity patterns based on Izhikevich model are studied. One of the connectivity patterns is a randomly connected neuronal network, the other one is a small-world neuronal network. This Izhikevich model is a simple model which can not only reproduce the rich behaviors of biological neurons but also has only two equations and one nonlinear term. Detailed investigations reveal that by varying some key parameters, such as the connection weights of neurons, the external current injection, the noise of intensity and the neuron number, this neuronal network will exhibit various collective behaviors in randomly coupled neuronal network. In addition, we show that by changing the number of nearest neighbor and connection probability in small-world topology can also affect the collective dynamics of neuronal activity. These results may be instructive in understanding the collective dynamics of mammalian cortex.

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“…This method has often been used in the study of the noisy dynamics of HH [10,18,42,43] and FitzHughNagumo (FHN) models [44] . To quantitatively characterize the regularity of the spike train, we have calculated the coefficient of variation (CV), defined as CV = Note that the appropriate change in detection threshold for a spike may not vary the qualitative spiking behavior.…”

Section: Model and Equationsmentioning

confidence: 99%

Enhancement of spike coherence by the departure from Gaussian noise in a Hodgkin-Huxley neuron

Xie

Gong

Hao

2009

Sci. China Ser. B-Chem.

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Experimental study has shown that non-Gaussian noise exists in sensory systems like neurons. The departure from Gaussian behavior is a characteristic parameter of non-Gaussian noise. In this paper, we have numerically studied the effect of a particular kind of non-Gaussian colored noise (NGN), especially its departure q from Gaussian noise (q = 1), on the spiking activity in a deterministic HodgkinHuxley (HH) neuron driven by sub-threshold periodic stimulus. Simulation results show that the departure q can affect the spiking activity induced by noise intensity D. For smaller q values, the minimum in the variation coefficient (CV) as a function of noise intensity (D) becomes smaller, showing that D-induced stochastic resonance (SR) becomes strengthened. Meanwhile, depending on the value of D, q can either enhance or reduce the spiking regularity. Interestingly, CV changes non-monotonously with varying q and passes through a minimum at an intermediate q, representing the presence of "departure-induced SR". This result shows that appropriate departures of the NGN can enhance the spike coherence in the HH neuron. Since the departure of the NGN determines the probability distribution and hence may denote the type of the noise, "departure-induced SR" shows that different types of noise can enhance the spike coherence, and hence may improve the timing precision of sub-threshold signal encoding in the HH neuron.neuron, non-Gaussian colored noise, spike coherence, stochastic resonance, stochastic dynamics

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Ordering spatiotemporal chaos in complex thermosensitive neuron networks

Cited by 61 publications

References 35 publications

Effects of channel noise on synchronization transitions in Newman–Watts neuronal network with time delays

Effects of channel noise on synchronization transitions in Newman–Watts neuronal network with time delays

Oscillations and synchrony in a cortical neural network

Enhancement of spike coherence by the departure from Gaussian noise in a Hodgkin-Huxley neuron

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