Neuromorphic computing using spike‐based learning has broad prospects in reducing computing power. Memristive neurons composed with two locally active memristors have been used to mimic the dynamical behaviors of biological neurons. Herein, the dynamic operating conditions of NbO2‐based memristive neurons and their transformation boundaries between the spiking and the bursting are comprehensively investigated. Furthermore, the underlying mechanism of bursting is analyzed, and the controllability of the number of spikes during each burst period is demonstrated. Finally, pattern classification and information transmitting in a perceptron neural network by using the number of spikes per bursting period to encode information is proposed. The results show a promising approach for the practical implementation of neuristor in spiking neural networks.
The dynamics of NbO2-based single and coupled oscillators are comprehensively investigated in this study. For single oscillators, the oscillation frequency is shown to have a strong dependence on the source voltage and load resistance. The range of the frequency modulation can be tuned from 2.1 to 6.8 MHz, while the load resistance is fixed at 3 kΩ. For coupled oscillators, rich and complex dynamics including in- and antiphase locking phenomena are carefully explored by using a mutual capacitor. According to different phase trajectories on the phase plane of both voltages across two devices, the evolution of the source voltage vs coupling capacitance phase diagram is shown with load resistance mismatch. Four coupling regions in the phase diagram are recognized, including a butterfly-shaped coupling zone, a linear coupling zone, a transition zone, and an uncoupled zone. As the load resistance mismatch increases from 1.0% to 3.9%, the linear coupling zone gradually disappears while the butterfly-shaped coupling zone gradually expands. When the load resistance mismatch is larger than 9.5%, the butterfly-shaped coupling zone gradually decreases while the uncoupled zone enlarges significantly.
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.
Nature positively embodies a rich yet complex array of nonlinear phenomena. To date, it has remained unclear how to exploit these phenomena to solve a wide range of problems. The Van der Pol oscillator is one of the nonlinear dynamical systems that hold tremendous promise for a broad range of important applications from a circuit performance booster to hard problem solving to mapping the biological nonlinear dynamics. Here, we theoretically build a Van der Pol oscillator circuit using a NbO2 volatile memristor to perform a systematic analysis of the complex nonlinear dynamic behavior. Three types of oscillation phenomena including period doubling, quasi-period, and chaos are obtained by varying the parallel capacitance and futher distinguished by mathematical analysis, such as fast Fourier transform, Poincaré plots, and plane trajectories of voltage on the memristor. The frequency locking phenomenon of the system is presented to enable a programmable frequency demultiplication. Moreover, the other critical circuit parameters such as DC voltage amplitude, load resistance, and AC driving frequency are also modulated to understand the nonlinear dynamic behavior of the system. All these analyses provide a viable platform to understand and implement nonlinear systems for a broad range of multifunctional oscillatory devices.
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