Numerical simulation of neural activity based on discrete Markov chains with stochastic differential equations

Abstract

With the development of new numerical calculation methods and computer software science and technology, people can have a good understanding of the potential mechanisms of cerebrovascular diseases. Here, we combine the stochastic differential equation (SDE) of discrete Markov chains to numerically simulate the dynamic changes of neural signals, and find that the changes of neural signals exhibit regular fluctuations. By analyzing the variation of voltage over time, we know that the voltage change at the next moment is closely related to the previous moment and has continuity. Based on the knowledge of neural ion channel dynamics, it was found that there will be longer peak changes in voltage, exhibiting a power-law distribution, which is consistent with the actual situation and statistical data related to resignation channels. By analyzing the voltage and peak changes of ion channels, we can gain a new understanding of the transmission laws of neural information and greatly improve the biological mechanisms.