Memristive effects on an improved discrete Rulkov neuron model

A change in neuronal-action potential can generate a magnetically induced current during the release and propagation of intracellular ions.To better characterize the electromagnetic-induction effect,this paper presents an improved discrete Rulkov(ID-Rulkov)neuron model by coupling a discrete model of a memristor with sine memductance into a discrete Rulkov neuron model.The ID-Rulkov neuron model possesses infinite invariant points,and its memristor-induced stability effect is evaluated by detecting the routes of period-doubling and Neimark-Sacker bifurcations.We investigated the memristor-induced dynamic effects on the neuron model using bifurcation plots and firing patterns.Meanwhile,we theoretically expounded the memristor initial-boosting mechanism of infinite coexisting patterns.The results show that the ID-Rulkov neuron model can realize diverse neuron firing patterns and produce hyperchaotic attractors that are nondestructively boosted by the initial value of the memristor,indicating that the introduced memristor greatly benefits the original neuron model.The hyperchaotic attractors initially boosted by the memristor were verified by hardware experiments based on a hardware platform.In addition,pseudorandom number generators are designed using the ID-Rulkov neuron model,and their high randomness is demonstrated based onstrict test results.