  Continuous time memristor have been widely used in fields such as chaotic
oscillating circuitsand neuromorphic computing systems, but research on the
application of discretememristors haven't been noticed yet. In this paper, we
designed a new chaoticneuron by applying the discrete model to two-dimensional
Rulkov chaotic neuron,and analyzed its dynamical behaviors by extensive
experiments involves phasediagram, bifurcation diagram, and spectral entropy
complexity algorithm. The experimental results show that the charge of the
memristor has an importanteffect on the system dynamics, delaying the
occurrence of bifurcation, and evenin the case of full memory, leading to the
disappearance of the bifurcation thus makethe system reach a fixed point.
Besides, the increase of the current magnification,can also bring about the
increase in discharge frequency ofneurons, and a wider and larger range of
spectral entropy complexity. The results of our study show the performance of
Rulkov chaotic neuronis improved by applying thediscrete memristor, and may
provide new insights into the mechanism of memoryand cognition in the nervous.
