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具有突触学习功能的兴奋性爆发式霍奇金-赫胥黎神经元网络的节律性振荡

Rhythmic Oscillations of Excitatory Bursting Hodkin-Huxley Neuronal Network with Synaptic Learning.

作者信息

Shi Qi, Han Fang, Wang Zhijie, Li Caiyun

机构信息

Engineering Research Center of Digitized Textile & Apparel Technology, College of Information Science and Technology, Donghua University, Shanghai 201620, China.

出版信息

Comput Intell Neurosci. 2016;2016:6023547. doi: 10.1155/2016/6023547. Epub 2016 Mar 17.

Abstract

Rhythmic oscillations of neuronal network are actually kind of synchronous behaviors, which play an important role in neural systems. In this paper, the properties of excitement degree and oscillation frequency of excitatory bursting Hodkin-Huxley neuronal network which incorporates a synaptic learning rule are studied. The effects of coupling strength, synaptic learning rate, and other parameters of chemical synapses, such as synaptic delay and decay time constant, are explored, respectively. It is found that the increase of the coupling strength can weaken the extent of excitement, whereas increasing the synaptic learning rate makes the network more excited in a certain range; along with the increasing of the delay time and the decay time constant, the excitement degree increases at the beginning, then decreases, and keeps stable. It is also found that, along with the increase of the synaptic learning rate, the coupling strength, the delay time, and the decay time constant, the oscillation frequency of the network decreases monotonically.

摘要

神经元网络的节律性振荡实际上是一种同步行为,在神经系统中起着重要作用。本文研究了包含突触学习规则的兴奋性爆发霍奇金 - 赫胥黎神经元网络的兴奋度和振荡频率特性。分别探讨了耦合强度、突触学习率以及化学突触的其他参数,如突触延迟和衰减时间常数的影响。研究发现,耦合强度的增加会减弱兴奋程度,而在一定范围内增加突触学习率会使网络更兴奋;随着延迟时间和衰减时间常数的增加,兴奋度开始增加,然后降低,并保持稳定。还发现,随着突触学习率、耦合强度、延迟时间和衰减时间常数的增加,网络的振荡频率单调下降。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5baf/4814680/c40a8061ca66/CIN2016-6023547.001.jpg

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