Evidence map›Paper›PMID 40678757›Full record

ArticleFrontiers in neuroscience2025

Study on the anti-interference characteristics of neuronal networks: a comparative study of chemical synapses and electrical synapse.

Xiang Li, Mai Lu

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Article in Frontiers in neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Xiang LiKey Laboratory of Opto-Electronic Technology and Intelligent Control, Ministry of Education, Lanzhou Jiaotong University, Lanzhou, Gansu, China.
Mai LuKey Laboratory of Opto-Electronic Technology and Intelligent Control, Ministry of Education, Lanzhou Jiaotong University, Lanzhou, Gansu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The synapses and network topology enhance neural synchronization and anti-interference, enabling the bio-inspired brain model to mimic biological noise resilience effectively. This study numerically simulates the effects of synapses and network topology on the synchronous discharge and anti-interference of neuronal networks. The Hodgkin-Huxley neuron model, the electrical synapses (ES), the Hansel chemical synapse (HS), and the Rabinovich chemical synapse (RS) were used to construct the neural networks with the ring structure and the Newman-Watts (NW) small-world topology. The sine wave and the sine wave with superimposed Gaussian white noise interference were selected as the stimulation signals. The MATLAB and Simulink platforms were employed to implement the numerical simulation. For the ring network with the sine wave stimulation, the correlation coefficients of one set of neuron pairs (neuron 1 and neuron 25) were 0.292 (ES), 0.236 (HS), and 0.168 (RS), respectively. However, after superimposed interference, the correlation coefficients become 0.099, 0.086, and 0.379, respectively. For the NW small-world topology with sinusoidal stimulation, the correlation coefficients of the same neuron pair were 0.569 (ES), 0.563 (HS), and 0.969 (RS), respectively. The correlation coefficients after superposition interference become 0.569, 0.163, and 0.88, respectively. The HS-coupled network exhibits severe signal latency (Ring network: Latency >200 ms, NW small-world network: Latency >150 ms). While RS-coupled network demonstrates dramatically reduced delays (<50 ms) across both topologies. The results suggest that the synchronization of the RS coupling network is much better than that of both ES and HS coupling networks. Ring networks coupled via HS demonstrate performance metrics comparable to those of ES-coupled ring networks, albeit with significant action potential propagation delays observed in both configurations. The NW small-world network can reduce the delay of signal transmission in the network by increasing the number of pathways. As network topological complexity increases, distal neurons demonstrate reduced spike timing variability and enhanced firing synchrony, collectively improving interference suppression efficacy.

Indexed as

anti-interferencecorrelation coefficientHH neuron modelneuro synapsessimulation research

Identifiers

PMID40678757
PMCPMC12267173

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.