Dynamics of Discrete Memristor-Based Rulkov Neuron

Abstract

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.

Keywords

• Discrete memristor
• Rulkov neuron
• spectral entropy complexity
• bifurcation
• phase diagram