Data-driven neuronal dynamics: Model identification and spiking analysis

Nanjing University of Aeronautics and Astronautics, Nanjing, China

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 23 April 2024
Accepted: 24 June 2024

Abstract

Neurons serve as the fundamental unit in the structure and functionality of the nervous system, and the large cluster network formed by their interconnection determines the perception, learning, emotion, memory and other behaviors of the organism. While prior studies of neurons primarily focused on costly experiments and feature-based modeling, recent advancements in computing power and algorithms have made it possible to identify and analyze system with data-driven approaches. This study introduces a data-driven method for the identification of neuronal systems, and the result shows that the method is of great precision for further study. In addition, in order to investigate the mechanisms underlying the transition between resting and active states, a finite difference scheme is developed to calculate the mean first exit time and spiking frequency of neuronal systems. Notably, we propose the concept of noise coupling effect in our study, which elaborates that the spiking of a neuronal dynamical system can occur more rapidly under less stringent conditions when driven by the potential fluctuation of both Gaussian white noise and L´evy noise.