  Hardware spiking neural networks hold the promise of realizing artificial
intelligence with high energy efficiency. In this context, solid-state and
scalable memristors can be used to mimic biological neuron characteristics.
However, these devices show limited neuronal behaviors and have to be
integrated in more complex circuits to implement the rich dynamics of
biological neurons. Here we studied a NbOx memristor neuron that is capable of
emulating numerous neuronal dynamics, including tonic spiking, stochastic
spiking, leaky-integrate-and-fire features, spike latency, temporal
integration. The device also exhibits phasic bursting, a property that has
scarcely been observed and studied in solid-state nano-neurons. We show that we
can reproduce and understand this particular response through simulations using
non-linear dynamics. These results show that a single NbOx device is sufficient
to emulate a collection of rich neuronal dynamics that paves a path forward for
realizing scalable and energy-efficient neuromorphic computing paradigms.
