Detecting bifurcation points in a memristive neuron model

In this paper, bifurcations of a memristive neuron model are analyzed. The system shows different limit cycles and chaotic attractors by varying external current. The focus of this paper is finding bifurcation points of the system and predicting them using critical slowing down indicators. The system has different tipping points such as transition from a period-2 limit cycle to period-3 limit cycle, period-3 limit cycle to period-6 limit cycle and limit cycle to chaos. Two critical slowing down indicators have been used to predict tipping points of the system. The first critical slowing down indicator is autocorrelation at lag-1 which cannot indicate bifurcation points of the system. The second one is Lyapunov exponent which shows acceptable results in prediction of bifurcation points of the memristive neuron model.