Local excitation–inhibition ratio for synfire chain propagation in feed-forward neuronal networks

Xinmeng Guo, Haitao Yu, Jiang Wang, Jing Liu, Yibin Cao and Bin Deng

Physica A: Statistical Mechanics and its Applications, 2017, vol. 482, issue C, 308-316

Abstract: A leading hypothesis holds that spiking activity propagates along neuronal sub-populations which are connected in a feed-forward manner, and the propagation efficiency would be affected by the dynamics of sub-populations. In this paper, how the interaction between local excitation and inhibition effects on synfire chain propagation in feed-forward network (FFN) is investigated. The simulation results show that there is an appropriate excitation–inhibition (EI) ratio maximizing the performance of synfire chain propagation. The optimal EI ratio can significantly enhance the selectivity of FFN to synchronous signals, which thereby increases the stability to background noise. Moreover, the effect of network topology on synfire chain propagation is also investigated. It is found that synfire chain propagation can be maximized by an optimal interlayer linking probability. We also find that external noise is detrimental to synchrony propagation by inducing spiking jitter. The results presented in this paper may provide insights into the effects of network dynamics on neuronal computations.

Keywords: Synfire chain; Feed-forward network; Signal propagation; Excitation–inhibition ratio (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:482:y:2017:i:c:p:308-316

DOI: 10.1016/j.physa.2017.04.040

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