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

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.