Characterization and modeling of spiking and bursting in experimental NbOx neuron

TL;DR

This paper demonstrates that a single NbOx memristor neuron can emulate diverse neuronal behaviors, including spiking and bursting, through experimental characterization and nonlinear dynamic simulations, advancing neuromorphic computing.

Contribution

It introduces a NbOx memristor neuron capable of replicating complex neuronal dynamics, including phasic bursting, which is rarely observed in solid-state nano-neurons.

Findings

NbOx memristor exhibits tonic and stochastic spiking.

The device demonstrates leaky-integrate-and-fire and spike latency behaviors.

Simulation explains the origin of phasic bursting in the device.

Abstract

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.