scholarly journals Spike-Timing-Dependent Plasticity With Axonal Delay Tunes Networks of Izhikevich Neurons to the Edge of Synchronization Transition With Scale-Free Avalanches

2019 ◽  
Vol 13 ◽  
Author(s):  
Mahsa Khoshkhou ◽  
Afshin Montakhab
Keyword(s):  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Scale Free ◽  
Synchronization Transition

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.

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.

scholarly journals Reinforced Random Walker meets Spike Timing Dependent Plasticity

2017 ◽  
Author(s):  
Mohammadreza Soltanipour ◽  
Hamed Seyed-allaei
Keyword(s):  
Complex Adaptive System ◽  
Long Term Potentiation ◽  
Spike Timing ◽  
The Other ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Scale Free ◽  
Random Walker ◽  
Complex Adaptive

AbstractWe blended Reinforced Random Walker (RRW) and Spike Timing Dependent Plasticity (STDP) as a minimalistic model to study plasticity of neural network. The model includes walkers which randomly wander on a weighted network. A walker selects a link with a probability proportional to its weight. If the other side of the link is empty, the move succeeds and link’s weight is strengthened (Long Term Potentiation). If the other side is occupied, then the move fails and the weight of the link is weakened (Long Term Depression). Depending on the number of walkers, we observed two phases: ordered (a few strong loops) and disordered (all links are alike). We detected a phase transition from disorder to order depending on the number of walkers. At the transition point, where there was a balance between potentiation and depression, the system became scale-free and histogram of weights was a power law. This work demonstrate how dynamic of a complex adaptive system can lead to critical behavior in its structure via a STDP-like rule.

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.

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