Spiking dynamic behaviors of NbO2 memristive neurons: A model study

Abstract

Neuromorphic computing based on spikes has broad prospects in energy-efficient computation. Memristive neuron in this study is composed of two volatile memristors that have been shown to exhibit rich biological neuronal dynamics. Here, we show spiking dynamic behaviors of NbO2 memristive neurons by a detailed simulation study. With a DC input voltage, the operation windows of both periodic oscillation and neuron-like action potential spikes are recognized in the resistance–voltage phase diagrams of NbO2 memristive neurons. With a voltage pulse as the input, the periodic oscillation region can be classified into three subregions including the spike-OFF, spike-ON, and meta-spike transition regions. When the memristive neuron operates in the meta-spike transition region, it can regulate the “ON” and “OFF” states of the oscillation circuit by changing the ending time of the input pulse. It implies that both the input signal and the output signal determine the state of the circuit. The demonstration of a phase matching method provides a useful way for controlling “ON” and “OFF” states of the periodic oscillation behavior of the memristive neuron. Moreover, the effect of the circuit parameters on the peak-to-valley amplitude of the output spikes with action potential is investigated. A stable and controllable waveform output can be regulated by changing the capacitance, incorporating a series resistor, and customizing the active memristor. All these results provide a reliable reference for implementing memristive neurons in neuromorphic computing.