Effect of Channel Block on Multiple Coherence Resonance Induced by Time Delay in Adaptive Neuronal Networks with Spike-Timing-Dependent Plasticity

2018 ◽  
Vol 17 (04) ◽  
pp. 1850036
Author(s):  
Huijuan Xie ◽  
Yubing Gong
Keyword(s):  
Time Delay ◽  
Neuronal Networks ◽  
Spike Timing ◽  
Neural Systems ◽  
Channel Block ◽  
Coherence Resonance ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Scale Free ◽  
Insight Into

In this paper, we study effect of channel block (CB) on multiple coherence resonance (MCR) in adaptive scale-free Hodgkin–Huxley neuronal networks with spike-timing-dependent plasticity (STDP). It is found that potassium CB suppresses MCR, but sodium CB can enhance MCR, and there is optimal sodium CB level by which MCR becomes most pronounced. In addition, STDP has a significant influence on the effect of CB on MCR. As adjusting rate [Formula: see text] of STDP increases, for potassium CB there is proper [Formula: see text] by which MCR is most pronounced; however, for sodium CB MCR is reduced. These findings could provide a new insight into effect of CB on information processing in neural systems.

Channel Noise-Enhanced Synchronization Transitions Induced by Time Delay in Adaptive Neuronal Networks with Spike-Timing-Dependent Plasticity

2018 ◽  
Vol 17 (02) ◽  
pp. 1850011 ◽  
Author(s):  
Huijuan Xie ◽  
Yubing Gong ◽  
Baoying Wang
Keyword(s):  
Time Delay ◽  
Noise Intensity ◽  
Neuronal Networks ◽  
Spike Timing ◽  
Average Degree ◽  
Neural Systems ◽  
Channel Noise ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Scale Free

In this paper, we numerically study the effect of channel noise on synchronization transitions induced by time delay in adaptive scale-free Hodgkin–Huxley neuronal networks with spike-timing-dependent plasticity (STDP). It is found that synchronization transitions by time delay vary as channel noise intensity is changed and become most pronounced when channel noise intensity is optimal. This phenomenon depends on STDP and network average degree, and it can be either enhanced or suppressed as network average degree increases depending on channel noise intensity. These results show that there are optimal channel noise and network average degree that can enhance the synchronization transitions by time delay in the adaptive neuronal networks. These findings could be helpful for better understanding of the regulation effect of channel noise on synchronization of neuronal networks. They could find potential implications for information transmission in neural systems.

Effect of Spike-Timing-Dependent Plasticity on Intrinsic Coherence Resonance in Newman–Watts Stochastic Hodgkin–Huxley Neuronal Networks

2016 ◽  
Vol 15 (04) ◽  
pp. 1650027 ◽  
Author(s):  
Huijuan Xie ◽  
Yubing Gong ◽  
Qi Wang
Keyword(s):  
Neuronal Networks ◽  
Electrical Coupling ◽  
Network Size ◽  
Spike Timing ◽  
Channel Noise ◽  
Coherence Resonance ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Optimal Network ◽  
Chemical Coupling

In this paper, we numerically study the effect of spike-timing-dependent plasticity (STDP) on coherence resonance (CR) induced by channel noise in adaptive Newman–Watts stochastic Hodgkin–Huxley neuron networks. It is found that STDP can either enhance or suppress the intrinsic CR when the adjusting rate of STDP decreases or increases. STDP can alter the effects of network randomness and network size on the intrinsic CR. Under STDP, for electrical coupling there are optimal network randomness and network size by which the intrinsic CR becomes strongest, however, for chemical coupling the intrinsic CR is always enhanced as network randomness or network size increases, which are different from the results for fixed coupling. These results show that the intrinsic CR of the neuronal networks can be either enhanced or suppressed by STDP, and there are optimal network randomness and network size by which the intrinsic CR becomes strongest. These findings could provide a new insight into the role of STDP for the information processing and transmission in neural systems.

Temporal Coherence Transitions Induced by Channel Noise in Scale-Free Neuronal Networks with Time Delay

2017 ◽  
Vol 16 (04) ◽  
pp. 1750031 ◽  
Author(s):  
Huijuan Xie ◽  
Yubing Gong
Keyword(s):  
Information Processing ◽  
Time Delay ◽  
Noise Intensity ◽  
Neuronal Networks ◽  
Temporal Coherence ◽  
Neural Systems ◽  
Channel Noise ◽  
Scale Free ◽  
Regulation Effect ◽  
Insight Into

In this paper, we study the effect of channel noise on the temporal coherence of scale-free Hodgkin–Huxley neuronal networks with time delay. It is found that the temporal coherence of the neuronal networks changes as channel noise intensity is varied in different ways depending on the range of channel noise intensity. The temporal coherence monotonically decreases with the increase of channel noise intensity for too small or too big channel noise intensity. However, for intermediate channel noise intensity it intermittently and rapidly becomes high and low as channel noise intensity is varied, exhibiting temporal coherence transitions. Moreover, this phenomenon is dependent on coupling strength and network average degree and becomes strongest when they are optimal. This result shows that channel noise has a regulation effect on the temporal coherence of the delayed neuronal networks by inducing temporal coherence transitions. This provides a new insight into channel noise for the information processing and transmission in neural systems.

scholarly journals Burst Synchronization in A Scale-Free Neuronal Network with Inhibitory Spike-Timing-Dependent Plasticity

2018 ◽  
Author(s):  
Sang-Yoon Kim ◽  
Woochang Lim
Keyword(s):  
Synaptic Plasticity ◽  
Network Architecture ◽  
Neuronal Population ◽  
Matthew Effect ◽  
Spike Timing ◽  
Synaptic Inhibition ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Scale Free ◽  
Burst Synchronization

We are concerned about burst synchronization (BS), related to neural information processes in health and disease, in the Barabasi-Albert scale-free network (SFN) composed of inhibitory bursting Hindmarsh-Rose neurons. This inhibitory neuronal population has adaptive dynamic synaptic strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP). In previous works without considering iSTDP, BS was found to appear in a range of noise intensities for fixed synaptic inhibition strengths. In contrast, in our present work, we take into consideration iSTDP and investigate its effect on BS by varying the noise intensity. Our new main result is to find occurrence of a Matthew effect in inhibitory synaptic plasticity: good BS gets better via LTD, while bad BS get worse via LTP. This kind of Matthew effect in inhibitory synaptic plasticity is in contrast to that in excitatory synaptic plasticity where good (bad) synchronization gets better (worse) via LTP (LTD). We note that, due to inhibition, the roles of LTD and LTP in inhibitory synaptic plasticity are reversed in comparison with those in excitatory synaptic plasticity. Moreover, emergences of LTD and LTP of synaptic inhibition strengths are intensively investigated via a microscopic method based on the distributions of time delays between the preand the post-synaptic burst onset times. Finally, in the presence of iSTDP we investigate the effects of network architecture on BS by varying the symmetric attachment degree l* and the asymmetry parameter Δl in the SFN.

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