Neuron system shock superimposed response based on catastrophe dynamics

Abstract

With the rapid development of computer science and artificial intelligence technology, the complexity and intelligence of the neural network models constructed by people have been greatly improved. When the complex neuron system is subjected to the impact of “catastrophic”, its original characteristics may be changed, and the consequences are difficult to predict. Catastrophe dynamics mainly studies the source of the sudden violent change of nature and human society and its evolution. The impact of the system can be divided into endogenous and exogenous shocks. In this article, catastrophe theory is used to study the neuron system. Based on the mean field model of Hurst and Sornette, introducing the weight parameters, mathematical models are constructed to study the response characteristics of the neuron system in face of exogenous shocks, endogenous shocks, and integrated shocks. The time characteristics of the shock response of the neuron system are discussed too, such as the instantaneous and long-term response of the system in face of shocks, the different response forms according to the weight or linear superposition, and the influence of adjusting parameters on the neuron system. The research result shows that the authoritarian coefficient and weight coefficient have a very important influence on the response of neuron system; By adjusting the two coefficients, the purpose of disaster prevention, self-healing protection and response reducing can be well achieved.