Transition from double coherence resonances to single coherence resonance in a neuronal network with phase noise

2015 ◽  
Vol 25 (12) ◽  
pp. 123124 ◽  
Author(s):  
Yanbing Jia ◽  
Huaguang Gu
Keyword(s):  
Phase Noise ◽  
Neuronal Network ◽  
Coherence Resonance

Coherence resonance in a modified FHN neuron with autapse and phase noise

2018 ◽  
Vol 32 (30) ◽  
pp. 1850332 ◽  
Author(s):  
Xiaoqiang Liu ◽  
Xiaoli Yang
Keyword(s):  
Numerical Simulation ◽  
Phase Noise ◽  
Electromagnetic Induction ◽  
Neuronal Model ◽  
Parameter Range ◽  
Feedback Gain ◽  
Coherence Resonance ◽  
Induction Current ◽  
Significant Phenomenon

The influences of phase noise together with autapse on the resonance dynamics in a modified FitzHugh–Nagumo (FHN) neuron are investigated by numerical simulation, where the neuronal model is in the environment of electromagnetic induction. First, it is found that phase noise can induce double coherence resonances, which is further confirmed to be robust to the feedback gain of induction current. Surprisingly, by individually changing the period of phase noise and the feedback gain, a resonance-like behavior also appears. Subsequently, the significant phenomenon of autapse-induced multiple coherence resonances is discovered. Moreover, the phenomenon of multiple coherence resonances can emerge at a broad parameter range of autaptic strength and autaptic delay.

Regulation of Chemical Autapse on an FHN-ML Neuronal System

2019 ◽  
Vol 29 (14) ◽  
pp. 1950202 ◽  
Author(s):  
Lianghui Qu ◽  
Lin Du ◽  
Honghui Zhang ◽  
Zilu Cao ◽  
Zichen Deng
Keyword(s):  
Phase Noise ◽  
Neuronal Network ◽  
Neuronal Firing ◽  
Resonance Phenomenon ◽  
Neuronal System ◽  
Discharge Mode ◽  
Neuronal Systems ◽  
High Level ◽  
Stochastic Resonance Phenomenon ◽  
Oscillatory Current

To explore the feasibility of physiological manipulation of autaptic structures, the effects of autaptic connections on an FHN-ML neuronal system with phase noise stimulation are studied systematically. Firstly, according to the dynamic analysis of the FHN-ML neuron model, a saddle-node bifurcation can occur on an invariant circle. Under the action of external oscillatory current with phase noise, the neuronal firing activity is sensitive to phase noise with less intensity, and an appropriate noise intensity can induce a significant stochastic resonance phenomenon. Secondly, the chemical autaptic function can effectively regulate the neuronal discharge activity. An inhibitory autapse can not only induce the transition from depolarized resting to periodic spiking, but can also induce the FHN-ML neuron suppressed by strong phase noise to generate a pronounced intermittent high-level burst-like discharge mode when the autaptic conductance is greater than 0.1. Finally, for a two-dimensional regular FHN-ML neuronal network, a small amount of autaptic structures can induce some special waveforms to restore the propagation of nerve impulses interrupted by phase noise disturbance. This indicates the significant regulation of autapses on spatial patterns of the FHN-ML neuronal network. The study can provide some theoretical guidance for building autaptic structures in local areas to modulate the dynamic behaviors of biological neuronal systems.

How electromagnetic induction and coupled delay affect stochastic resonance in a modified neuronal network subject to phase noise

2019 ◽  
Vol 33 (26) ◽  
pp. 1950302
Author(s):  
Xiao Li Yang ◽  
Xiao Qiang Liu
Keyword(s):  
Time Delay ◽  
Phase Noise ◽  
Stochastic Resonance ◽  
Neuronal Network ◽  
Electromagnetic Induction ◽  
Small World ◽  
Feedback Gain ◽  
Small World Network ◽  
Induced Current ◽  
Resonance Response

Through introducing the ingredients of electromagnetic induction and coupled time delay into the original Fitzhugh–Nagumo (FHN) neuronal network, the dynamics of stochastic resonance in a model of modified FHN neuronal network in the environment of phase noise is explored by numerical simulations in this study. On one hand, we demonstrate that the phenomenon of stochastic resonance can appear when the intensity of phase noise is appropriately adjusted, which is further verified to be robust to the edge-added probability of small-world network. Moreover, under the influence of electromagnetic induction, the phase noise-induced resonance response is suppressed, meanwhile, a large noise intensity is required to induce stochastic resonance as the feedback gain of induced current increases. On the other hand, when the coupled time delay is incorporated into this model, the results indicate that the properly tuned time delay can induce multiple stochastic resonances in this neuronal network. However, the phenomenon of multiple stochastic resonances is found to be restrained upon increasing feedback gain of induced current. Surprisingly, by changing the period of phase noise, multiple stochastic resonances can still emerge when the coupled time delay is appropriately tuned to be integer multiples of the period of phase noise.

SPATIO-TEMPORAL COHERENCE RESONANCE AND FIRING SYNCHRONIZATION IN A NEURAL NETWORK: NOISE AND COUPLING EFFECTS

2009 ◽  
Vol 20 (03) ◽  
pp. 469-478 ◽  
Author(s):  
YANHONG ZHENG ◽  
QISHAO LU ◽  
QINGYUN WANG
Keyword(s):  
Neural Network ◽  
Neuronal Network ◽  
Coupling Strength ◽  
Noise Intensity ◽  
Temporal Coherence ◽  
Square Lattice ◽  
Collective Phenomena ◽  
Coherence Resonance ◽  
Critical Relation ◽  
Spatio Temporal

Effects of noise and coupling on the dynamics of a square lattice neuronal network are studied in this paper. Patterns and collective phenomena such as firing synchronization are investigated in networks with dynamics of each neuron described by FitzHugh–Nagumo model. As the noise intensity is increased, typical patterns emerge spatially, which propagate through the networks in the form of circular waves. Further increasing noise can destroy the circular wave, and then some random portraits appear. Moreover, the spatio-temporal coherence and the transitions of firing synchronization characterized by the rate of firing are investigated as the noise intensity and the coupling strength vary. The maximal coherence of the oscillations could be found at two optimal noise intensities (or coupling strength) for appropriate coupling strength (or noise intensity), displaying coherence bi -resonance. Finally, the critical relation between the noise intensity and the coupling strength is given to investigate the occurrence of firing synchronization in the network.

Phase noise-induced double coherence resonances in a neuronal model

2015 ◽  
Vol 29 (20) ◽  
pp. 1550142 ◽  
Author(s):  
Yanbing Jia ◽  
Huaguang Gu
Keyword(s):  
Phase Noise ◽  
Interspike Interval ◽  
Neuronal Model ◽  
Oscillation Period ◽  
Firing Frequency ◽  
Coherence Resonance ◽  
Firing Period

Phase noise-induced single coherence resonance (CR) has been reported in previous studies. It is reported here that double CRs can be induced in the FitzHugh–Nagumo (FHN) model by phase noise when the oscillation period of phase noise is much larger than the firing period of the FHN model. By analyzing peaks in the power spectrums for the fast voltage variable and the coefficient variations (CVs) of interspike interval (ISI) series, we find that double CRs corresponding to the frequency of phase noise and the firing frequency of the FHN model respectively appear at small and large noise intensities. This implies that there are double chances for the FHN model to take advantage of the benefits of phase noise. Possible causes of the single CR are also discussed.

Coherence resonance in stimulated neuronal network

2018 ◽  
Vol 106 ◽  
pp. 80-85 ◽  
Author(s):  
Andrey V. Andreev ◽  
Vladimir V. Makarov ◽  
Anastasija E. Runnova ◽  
Alexander N. Pisarchik ◽  
Alexander E. Hramov
Keyword(s):  
Neuronal Network ◽  
Coherence Resonance

Phase noise-induced coherence resonance in three dimension memristive Hindmarsh-Rose neuron model

2019 ◽  
Vol 228 (10) ◽  
pp. 2101-2110 ◽  
Author(s):  
Lulu Lu ◽  
Chun Bao ◽  
Mengyan Ge ◽  
Ying Xu ◽  
Lijian Yang ◽  
...  
Keyword(s):  
Phase Noise ◽  
Neuron Model ◽  
Three Dimension ◽  
Coherence Resonance

Spatial coherence resonance and spatial pattern transition induced by the decrease of inhibitory effect in a neuronal network

2017 ◽  
Vol 31 (26) ◽  
pp. 1750179 ◽  
Author(s):  
Ye Tao ◽  
Huaguang Gu ◽  
Xueli Ding
Keyword(s):  
Spatial Patterns ◽  
Neuronal Network ◽  
Brain Cortex ◽  
Pyramidal Neurons ◽  
Spatial Coherence ◽  
Inhibitory Effect ◽  
Spiral Waves ◽  
Inhibitory Interneurons ◽  
Coherence Resonance ◽  
Biological Experiment

Spiral waves were observed in the biological experiment on rat brain cortex with the application of carbachol and bicuculline which can block inhibitory coupling from interneurons to pyramidal neurons. To simulate the experimental spiral waves, a two-dimensional neuronal network composed of pyramidal neurons and inhibitory interneurons was built. By decreasing the percentage of active inhibitory interneurons, the random-like spatial patterns change to spiral waves and to random-like spatial patterns or nearly synchronous behaviors. The spiral waves appear at a low percentage of inhibitory interneurons, which matches the experimental condition that inhibitory couplings of the interneurons were blocked. The spiral waves exhibit a higher order or signal-to-noise ratio (SNR) characterized by spatial structure function than both random-like spatial patterns and nearly synchronous behaviors, which shows that changes of the percentage of active inhibitory interneurons can induce spatial coherence resonance-like behaviors. In addition, the relationship between the coherence degree and the spatial structures of the spiral waves is identified. The results not only present a possible and reasonable interpretation to the spiral waves observed in the biological experiment on the brain cortex with disinhibition, but also reveal that the spiral waves exhibit more ordered degree in spatial patterns.

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