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Journal of Zhejiang University SCIENCE A 2019 Vol.20 No.9 P.639-659


A physical view of computational neurodynamics

Author(s):  Jun Ma, Zhuo-qin Yang, Li-jian Yang, Jun Tang

Affiliation(s):  Department of Physics, Lanzhou University of Technology, Lanzhou 730050, China; more

Corresponding email(s):   hyperchaos@163.com, hyperchaos@lut.cn

Key Words:  Neuron, Neural networks, Autapse, Hamilton energy, Electromagnetic induction

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Jun Ma, Zhuo-qin Yang, Li-jian Yang, Jun Tang. A physical view of computational neurodynamics[J]. Journal of Zhejiang University Science A, 2019, 20(9): 639-659.

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author="Jun Ma, Zhuo-qin Yang, Li-jian Yang, Jun Tang",
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publisher="Zhejiang University Press & Springer",

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%A Jun Ma
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%A Jun Tang
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T1 - A physical view of computational neurodynamics
A1 - Jun Ma
A1 - Zhuo-qin Yang
A1 - Li-jian Yang
A1 - Jun Tang
J0 - Journal of Zhejiang University Science A
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A1900273

The nervous system is made of a large number of neurons. Time-varying balance between excitatory and inhibitory neurons is important to activate appropriate modes of electrical activity. A realistic biological neuron is complex, often presenting various electrophysiological activities and diffusive propagation of ions in the cell. Therefore, the physical effects of electromagnetic induction become very important and should be considered when estimating signal encoding and mode selection. Synaptic plasticity and anatomical structure have been developed to enhance the self-adaption of neurons. Thus, the electrical mode with the most effective links and weights can be selected to benefit information encoding and signal propagation between neurons in the network. As a result, the demand for metabolic energy can be greatly reduced. In this review, neuron model setting with biophysical effects, modulation of astrocytes, autapse formation and biological function, synaptic plasticity, memristive synapses, and field coupling between neurons and networks are reviewed briefly to provide guidance in the field of neurodynamics.

Authors have presented a detail review of neuron model, astrocyte, synaptic plasticity, collective behaviors in neural networks from a physical view point. Different examples have been proposed to illustrate authors' aim. The manuscript is attractive.


目的:基于物理学基本原理解释神经元电活动过程中存在的物理效应,解释突触生物功能活化过程的物理机制,以及分析神经元建模中的电磁场效应(图1). 探讨神经元建模、胶质细胞调控、突触可塑性和神经元群体电活动的网络效应.
创新点:1. 论证荷控和磁控忆阻器非线性函数在物理神经元模型构建中的作用. 2. 提出神经元突触耦合的物理机制就是电场和磁场耦合(图3). 3. 研究神经元电路混合突触耦合的物理实现(图2)以及能量存储与泵浦.
方法:依据物理学电磁感应定律和赫姆霍兹定理论证神经元电活动过程产生的电磁感应效应以及能量输运过程. 基于忆阻器物理特性和量纲一致原理来构建物理神经元模型,从物理角度解释突触功能实现过程的物理机制.
结论:在神经元电活动过程中需考虑电磁感应效应; 场耦合可以调控神经元突触耦合作用; 在神经元网络中信号传递需考虑物理场耦合过程.

关键词:神经元; 神经网络; 自突触; 哈密顿能量; 电磁感应

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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