Artificial Axon with Dendritic-like Plasticity by Biomimetic Interface Engineering of Anisotropic Two-Dimensional Tellurium

Abstract

Spiking neural network (SNN) hardware relies on implicit assumptions that prioritize dendritic/synaptic learning above axon/synaptic concerns, compromising performances in signal capacity, accuracy, and compactness of SNN systems. Herein, we develop an artificial axon by utilizing the heterogeneity and interface state tunability in anisotropic two-dimensional (2D) tellurium (Te). By operating a multiterminal axon under the bioelectricity level, the device achieved neuron-like heterogeneous axon dynamics expansion (∼258%). An excellent dendritic-like tunability (∼197%) exhibits gain on the axons. The synergistic axon–dendrite optimization device exhibits 5-bit programmable conductance, signal filtering, and input enhancing. The accuracy of recognizing data sets based on the SNN algorithm demonstrates efficient optimization (5.2% higher accuracy) of networks by the device features, especially in the case of performing image preprocessing. This artificial neuron solution with anisotropic 2D materials utilizing biomimetic interface engineering provides a universal strategy for compact, high-precision parallel architecture of SNN hardware.