生物系统
能源消耗
轴突
神经元
舱室(船)
生物神经元模型
前馈
计算机科学
稳健性(进化)
神经科学
前馈神经网络
人工神经网络
控制理论(社会学)
生物
人工智能
生态学
工程类
控制(管理)
地质学
生物化学
海洋学
控制工程
基因
作者
Qianming Ding,Yong Wu,Tianyu Li,Dong Yu,Ya Jia
标识
DOI:10.1016/j.chaos.2023.113464
摘要
The physiological structure of the brain and the morphology of its neurons result from selective pressure, the nervous system must be energy efficient. In this paper, a two compartment model consisting of dendritic and axon compartments is proposed to simulate the effects of morphology and temperature on neuronal kinetics. Based on the equivalent circuit method, we calculated the metabolic energy consumption of neurons with morphological and temperature variations. It is shown that higher temperatures increase the separation of Na+ and K+ currents in the axon, resulting in higher energy efficiency, while neuron with moderate morphological parameter is more likely to fire, but accompanied by a higher energy cost. We integrate this two-compartment model into a feedforward network to simulate changes in network energy consumption affected by neuron morphology and temperature during information propagation. It is found that moderate temperature and morphology allow stable propagation of synchronization spikes in the feedforward network and maximize the energy utilization for information transmission. In addition, we present a simple statistical method to detect the robustness of network information transmission. This paper may provide some insights for further studies on energy consumption in the cerebral cortex.
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