Volatile threshold switching and neural dynamics emulation in a chitosan–ZnO memristor for neuromorphic computing

Abstract

Neuromorphic computing demands energy-efficient and biologically plausible devices to emulate neural dynamics and sensory processing. This study explores the development and application of a chitosan-doped ZnO memristor for neuromorphic computing, focusing on its volatile threshold switching behavior and bio-inspired sensory applications. The device exhibits excellent memristive performance, with a high switching ratio (∼105), stable endurance (>104 cycles), and rapid switching speeds (turn-on/turn-off times of ∼23/21 µs). Symmetric threshold voltages (±2 V) and low resistance variability highlight its reliability. Integrated into an oscillatory neuron circuit (R–C configuration), the memristor emulates spiking dynamics, demonstrating tunable frequency and energy efficiency (∼832 nJ/spike). Furthermore, the circuit successfully replicates biological motion detection and sound localization by processing spatiotemporal input differences, mimicking direction-selective ganglion cells and medial superior olive neurons. These results validate the memristor’s potential for bio-inspired sensory systems, offering a scalable, energy-efficient platform for neuromorphic computing and artificial perception.