LAUSR

The anti-interference characteristics of the neural network have a key impact on its information processing ability in complex environments. Most of the existing research focuses on small-scale networks and simplified models, and there is still a lack of systematic discussion on the influence mechanism of large-scale network expansion and topological complexity. In this study, a large-scale neural network model with different topologies is constructed to explore the influence mechanism of network size and connection complexity on the anti-disturbance characteristics. The optimal synchronization characteristics of complex NW small-world networks under noise interference are revealed, which provides a theoretical reference for the topology design and anti-interference ability improvement of artificial neural networks. Based on the Hodgkin-Huxley neuron dynamics model and Leonid chemical synapse theory, a complex Newman-Watts (NW) small-world network model containing 500 neurons is established for the first time, and the dynamic response characteristics of three topologies of simple ring network, simple NW small-world and complex NW small-world under music noise interference are compared and analyzed. The signal synchronization of the network is quantitatively evaluated by Pearson correlation, and the variation law of the anti-interference performance of different topologies is systematically revealed when the scale of the neural network is expanded from 100 to 500 neurons. The research shows that the expansion of network size and the increase of topological connection complexity can significantly enhance the anti-interference performance of neural network. Among them, the complex NW small-world network performs best in the noise interference environment, and the correlation coefficient increases significantly at the scale of 500 neurons. In this study, the network scale is extended to 500 neurons for the first time. By constructing a complex NW small-world topology, the influence of scale expansion and connection complexity improvement on the network anti-interference performance is systematically quantified, which provides reference simulation data for the simulation research of artificial neural networks.