Coherence resonance in the two-dimensional neural map driven by non-Gaussian colored noise

2016 ◽  
Vol 30 (05) ◽  
pp. 1650012 ◽  
Author(s):  
Dongxi Li ◽  
Bing Hu ◽  
Jia Wang ◽  
Yingchuan Jing ◽  
Fangmei Hou
Keyword(s):  
Correlation Time ◽  
Discrete System ◽  
Noise Intensity ◽  
Colored Noise ◽  
Two Dimensional ◽  
Coherence Resonance ◽  
Firing Activity ◽  
Gaussian Colored Noise ◽  
Coherence Parameter ◽  
Non Gaussian

Based on the two-dimensional (2D) neural map, we investigate the impacts of non-Gaussian colored noise on the firing activity of discrete system. Taking the coherence parameter R to measure the regularity of firing behavior, it is demonstrated that coherence parameter R has a pronounced minimum value with the noise intensity and the correlation time of non-Gaussian colored noise, which is the so-called phenomenon of coherence resonance (CR). Besides, the firing activity is not sensitive to the non-Gaussian parameter which determines the departure from the Gaussian distribution when the correlation time is large enough.

NON-GAUSSIAN NOISE-INDUCED FIRING TRANSITIONS AND ORDERED BURSTING IN A THERMO-SENSITIVE NEURON

2010 ◽  
Vol 09 (03) ◽  
pp. 289-299 ◽  
Author(s):  
YUBING GONG ◽  
XIU LIN ◽  
YINGHANG HAO ◽  
YANHANG XIE ◽  
XIAOGUANG MA
Keyword(s):  
Information Processing ◽  
Gaussian Noise ◽  
Correlation Time ◽  
Colored Noise ◽  
Firing Activity ◽  
Firing Behavior ◽  
Gaussian Colored Noise ◽  
Non Gaussian

In this letter, we investigate how a particular kind of non-Gaussian colored noise (NGN), especially the correlation time τ and the departure q from Gaussian noise, affects the chaotic firing behavior in a thermo-sensitive neuron. It is found that transitions between spiking and bursting occur with changing τ or q, and ordered bursting appears when τ is optimal. As τ is increased, the neuron alternately exhibits spiking and bursting when q < 1, but always bursts when q > 1, and chaotic bursts may become ordered at an optimal τ. As q is increased, the neuron also exhibits transitions between spiking and bursting. These findings provide a new mechanism for the firing transitions in the neuron and present the constructive role of the NGN in the firing activity in the neuron. This reveals that the NGN would play subtle roles in the communication and information processing in the neurons.

Nano-friction phenomenon of Frenkel-Kontorova model under Gaussian colored noise

2021 ◽  
Author(s):  
Li Yi-Wei ◽  
Xu Peng-Fei ◽  
Yang Yong-Ge
Keyword(s):  
Friction Force ◽  
Correlation Time ◽  
Noise Intensity ◽  
Colored Noise ◽  
Static Friction ◽  
Simulation Method ◽  
Stochastic Force ◽  
Static Friction Force ◽  
Gaussian Colored Noise ◽  
Kontorova Model

Abstract The nano-friction phenomenon in a one-dimensional Frenkel-Kontorova model under Gaussian colored noise is investigated by using the molecular dynamic simulation method. The role of colored noise is analyzed through the inclusion of a stochastic force via a Langevin molecular dynamics method. Via the stochastic Runge-Kutta algorithm, the relationship between different parameter values of the Gaussian colored noise (the noise intensity and the correlation time) and the nano-friction phenomena such as hysteresis, the maximum static friction force is separately studied here. Similar results are obtained from the two geometrically opposed ideal cases: incommensurate and commensurate interfaces. It was found that the noise strongly influences the hysteresis and maximum static friction force and with an appropriate external driving force, the introduction of noise can accelerate the motion of the system, making the atoms escape from the substrate potential well more easily. Interestingly, suitable correlation time and noise intensity give rise to super-lubricity. It is noteworthy that the difference between the two circumstances lies in the fact that the effect of the noise is much stronger on triggering the motion of the FK model for the commensurate interface than that for the Incommensurate interface.

ENHANCEMENT OF INTRINSIC SPIKING COHERENCE BY EXTERNAL NON-GAUSSIAN NOISE IN A STOCHASTIC HODGKIN–HUXLEY NEURON

2011 ◽  
Vol 10 (04) ◽  
pp. 359-369 ◽  
Author(s):  
LI WANG ◽  
YUBING GONG ◽  
XIU LIN
Keyword(s):  
Information Processing ◽  
Gaussian Distribution ◽  
Gaussian Noise ◽  
Correlation Time ◽  
Temporal Coherence ◽  
Channel Noise ◽  
Coherence Resonance ◽  
Non Gaussian ◽  
Proper Values ◽  
Membrane Patch

In this paper, we study the effect of external non-Gaussian noise on the temporal coherence of the intrinsic spiking induced by the channel noise in a stochastic Hodgkin–Huxley neuron. It is found that, for a sufficiently large membrane patch, the intrinsic spiking coherence can be enhanced by the proper values of non-Gaussian noise's strength, correlation time, or deviation from Gaussian distribution. And that the intrinsic spiking can exhibit coherence resonance when the noise's strength is optimal. This implies that the channel noise-induced intrinsic spiking may become more or the most ordered in time with the assistance of the external non-Gaussian noise. These results show that the external non-Gaussian noise can play a constructive role for improving the time precision of information processing in stochastic neurons.

Asymmetric stochastic resonance under non-Gaussian colored noise and time-delayed feedback

2020 ◽  
Vol 29 (5) ◽  
pp. 050501 ◽  
Author(s):  
Ting-Ting Shi ◽  
Xue-Mei Xu ◽  
Ke-Hui Sun ◽  
Yi-Peng Ding ◽  
Guo-Wei Huang
Keyword(s):  
Stochastic Resonance ◽  
Colored Noise ◽  
Delayed Feedback ◽  
Gaussian Colored Noise ◽  
Non Gaussian ◽  
Time Delayed Feedback

SELECTION OF SPIRAL WAVE IN THE COUPLED NETWORK UNDER GAUSSIAN COLORED NOISE

2013 ◽  
Vol 27 (21) ◽  
pp. 1350115 ◽  
Author(s):  
FAN LI ◽  
JUN MA
Keyword(s):  
Correlation Time ◽  
Colored Noise ◽  
Nearest Neighbor ◽  
Spiral Wave ◽  
External Noise ◽  
Parameter Region ◽  
Chua Circuit ◽  
Gaussian Colored Noise ◽  
Connection Type ◽  
Selection Of

The selection and breakup of spiral wave in a coupled network is investigated by imposing Gaussian colored noise on the network, respectively. The dynamics of each node of the network is described by a simplified Chua circuit, and nodes are uniformly placed in a two-dimensional array with nearest-neighbor connection type. The transition of spiral wave is detected by changing the coupling intensity, intensity and correlation time τ in the noise. A statistical variable is used to discern the parameter region for breakup of spiral wave and robustness to external noise. Spiral waves emerge in the network when the network with structure of complex-periodic and chaotic properties. It is found that asymmetric coupling can induce deformation of spiral wave, stronger intensity or smaller correlation time in noise does cause breakup of the spiral wave.

scholarly journals Stochastic Resonance in Neuronal Network Motifs with Ornstein-Uhlenbeck Colored Noise

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Xuyang Lou
Keyword(s):  
Stochastic Resonance ◽  
Correlation Time ◽  
Noise Intensity ◽  
Colored Noise ◽  
Stochastic Dynamics ◽  
Signal To Noise Ratio ◽  
Network Motif ◽  
Control Parameters ◽  
Noise Process ◽  
Chemical Coupling

We consider here the effect of the Ornstein-Uhlenbeck colored noise on the stochastic resonance of the feed-forward-loop (FFL) network motif. The FFL motif is modeled through the FitzHugh-Nagumo neuron model as well as the chemical coupling. Our results show that the noise intensity and the correlation time of the noise process serve as the control parameters, which have great impacts on the stochastic dynamics of the FFL motif. We find that, with a proper choice of noise intensities and the correlation time of the noise process, the signal-to-noise ratio (SNR) can display more than one peak.

Sign in / Sign up

Export Citation Format

Share Document