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Hardware spiking neural networks hold the promise of realizing artificialintelligence with high energy efficiency. In this context, solid-state andscalable memristors can be used to mimic biological neuron characteristics.However, these devices show limited neuronal behaviors and have to beintegrated in more complex circuits to implement the rich dynamics ofbiological neurons. Here we studied a NbOx memristor neuron that is capable ofemulating numerous neuronal dynamics, including tonic spiking, stochasticspiking, leaky-integrate-and-fire features, spike latency, temporalintegration. The device also exhibits phasic bursting, a property that hasscarcely been observed and studied in solid-state nano-neurons. We show that wecan reproduce and understand this particular response through simulations usingnon-linear dynamics. These results show that a single NbOx device is sufficientto emulate a collection of rich neuronal dynamics that paves a path forward forrealizing scalable and energy-efficient neuromorphic computing paradigms.

Characterization and modeling of spiking and bursting in experimental NbOx neuron