Artificial Spiking Neuron with Bursting Dynamics for Noise-Resistant Neuromorphic Coding

Emulating complex neuronal functionalities in human brainsusingemerging biomimetic electronics is critical for next-generation brain-likechips and artificial general intelligence. Burst firing, featuredwith the repeated firing of discrete groups of spikes, is high-orderneuronal dynamics for reliable signal transmission, coding, and processing.Here, we report a memristor (Pt/Co3O4-x /ITO) that can natively produce periodic voltageoscillation groups under current stimulation, capturing the burst-firingfeatures of actual biological neurons. This behavior stems from thedynamic formation/rupture of conductive filaments regulated by the V (O) concentration at the grain boundaries. Theburst frequency can be tuned by adjusting the driving current intensityand used to implement burst frequency coding. The bursting neuron-basedartificial neural system achieves a recognition accuracy of 86% forthe Fashion-MNIST tasks, under the disturbance of random noise spikes.This work provides a promising platform to realize complex neuronaldynamics and to construct robust brain-inspired neuromorphic systems.

Automated summary

This study presents a memristor that can mimic the burst-firing features of biological neurons by generating periodic voltage oscillation groups. The burst frequency can be adjusted and used for burst frequency coding. The artificial neural system based on bursting neurons achieves high recognition accuracy for Fashion-MNIST tasks.