CLC number: O59; TN710
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2019-08-06
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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.
@article{title="A physical view of computational neurodynamics",
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journal="Journal of Zhejiang University Science A",
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number="9",
pages="639-659",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1900273"
}
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A1900273
Abstract: 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.
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