Effect of spike-timing-dependent plasticity on stochastic burst synchronization in a scale-free neuronal network

Cognitive Neurodynamics
Sang-Yoon Kim, Woochang Lim

Abstract

We consider an excitatory population of subthreshold Izhikevich neurons which cannot fire spontaneously without noise. As the coupling strength passes a threshold, individual neurons exhibit noise-induced burstings. This neuronal population has adaptive dynamic synaptic strengths governed by the spike-timing-dependent plasticity (STDP). However, STDP was not considered in previous works on stochastic burst synchronization (SBS) between noise-induced burstings of sub-threshold neurons. Here, we study the effect of additive STDP on SBS by varying the noise intensity D in the Barabási-Albert scale-free network (SFN). One of our main findings is a Matthew effect in synaptic plasticity which occurs due to a positive feedback process. Good burst synchronization (with higher bursting measure) gets better via long-term potentiation (LTP) of synaptic strengths, while bad burst synchronization (with lower bursting measure) gets worse via long-term depression (LTD). Consequently, a step-like rapid transition to SBS occurs by changing D, in contrast to a relatively smooth transition in the absence of STDP. We also investigate the effects of network architecture on SBS by varying the symmetric attachment degree [Formula: see text] and the a...Continue Reading

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Citations

Feb 6, 2020·Cognitive Neurodynamics·Sang-Yoon Kim, Woochang Lim
Jun 7, 2019·Cognitive Neurodynamics·Osamu Nakamura, Katsumi Tateno
Feb 6, 2020·Cognitive Neurodynamics·Zuolei Wang, Xuerong Shi
May 14, 2020·Cognitive Neurodynamics·Jia ZhaoJingwen Li

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