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

Tianyu Li, Yong Wu, Lijian Yang, Ziying Fu and Ya Jia

Chaos, Solitons & Fractals, 2023, vol. 172, issue C

Abstract: 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 necessary for the propagation of weak signals. Excessively strong or weak chaotic current leads to propagation failure. Sparse connection and weak synaptic strength optimize the responses of the output layer, which is consistent with real biological networks observed in the brain. It is found that weak signal propagation in FFN is highly correlated with the regulation of firing rate. Our results may provide novel insights into the modeling of complex networks and network function implementation.

Keywords: Multi-layer feedforward network; Weak signal propagation; Neuronal morphology; Network properties (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:172:y:2023:i:c:s0960077923004551

DOI: 10.1016/j.chaos.2023.113554

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