Dynamics of Discrete Memristor-Based Rulkov Neuron

Continuous-time memristor have been widely used in fields such as chaotic circuits and neuromorphic computing systems, however, research on the application of discrete memristors haven't been noticed yet. In this paper, a new chaotic neuron is firstly designed by applying the discrete memristor to two-dimensional Rulkov neuron. And then the dynamical behaviors of the discrete memristor-based neuron are analyzed by experiments including phase diagram, bifurcation, and spectral entropy complexity algorithm. The results show that the resistance of memristor has an important effect on the system dynamics, which delays the occurrence of bifurcation, in particular, the bifurcation disappears and the system reaches the fixed point of the neuron when the resistance is greater than a threshold. It is also found that with the increase of the current gain, the bursting activity becomes higher in frequency and wider range of high complexity is obtained. The results of our study show that the performance of Rulkov neuron is improved by applying the discrete memristor, and may provide new insights into the mechanism of memory and cognition in the nervous.

Automated summary

This paper designs a new chaotic neuron by applying the discrete memristor to a two-dimensional Rulkov neuron and analyzes its dynamical behaviors through experiments. The resistance of memristor is found to have an important effect on the system dynamics, while the increase of current gain leads to higher bursting activity frequency and wider range of high complexity.