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Neuronal morphology and network properties modulate signal propagation in multi-layer feedforward network

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成果类型:
期刊论文
作者:
Li, Tianyu;Wu, Yong;Yang, Lijian;Fu, Ziying;Jia, Ya
通讯作者:
Jia, Y
作者机构:
[Wu, Yong; Yang, Lijian; Jia, Ya; Li, Tianyu] Cent China Normal Univ, Dept Phys, Wuhan 430079, Peoples R China.
[Fu, Ziying] Cent China Normal Univ, Sch Psychol, Wuhan 430079, Peoples R China.
通讯机构:
[Jia, Y ] C
Cent China Normal Univ, Dept Phys, Wuhan 430079, Peoples R China.
语种:
英文
关键词:
Multi-layer feedforward network;Weak signal propagation;Neuronal morphology;Network properties
期刊:
CHAOS SOLITONS & FRACTALS
ISSN:
0960-0779
年:
2023
卷:
172
页码:
113554
基金类别:
National Natural Science Foundation of China [12175080]; Funda- mental Research Funds for the Central Universities [CCNU22JC009]
机构署名:
本校为第一且通讯机构
院系归属:
物理科学与技术学院
心理学院
摘要:
The propagation and detection of weak signals play a vital role in the central nervous system's information processing. In this paper, a biophysical two-compartment model is adopted to investigate how the neuronal morphology and network properties modulate signal propagation in a multi-layer feedforward network (FFN). The numerical simulation results show that neurons with larger dendrites have higher firing rates and better responses to weak signals. Similarly, the output layer of FFN constructed by larger-dendrite neurons also exhibits better responses. A suitable chaotic current is necessar...

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