A Neuransistor with Excitatory and Inhibitory Neuronal Behaviors for Liquid State Machine

Abstract

Abstract A liquid state machine (LSM) is a spiking neural network model inspired by biological neural network dynamics designed to process time‐varying inputs. In the LSM, maintaining a proper excitatory/inhibitory (E/I) balance among neurons is essential for ensuring network stability and generating rich temporal dynamics for accurate data processing. In this study, a “neuransistor” is proposed that implements the E/I neurons in a single device, allowing for the hardware implementation of the LSM. The device features a three‐terminal transistor structure embodying TiO 2− x /Al 2 O 3 bi‐layer, providing a two‐dimensional electron electron gas (2DEG) channel at their interface. This device demonstrates hybrid excitatory and inhibitory dynamics with respect to the applied gate bias polarity, originating from the charge trapping/detrapping between the 2DEG and TiO 2− x layers. Additionally, the three‐terminal configuration allows masking capabilities by selecting terminal biases, realizing a reservoir behavior with superior reliability and durability. Its use in an LSM reservoir for time‐series data prediction tasks using the Henon dataset and a chaotic equation solver for the Lorenz attractor is demonstrated. This benchmarking indicates that the LSM exhibits enhanced performance and efficiency compared to the conventional echo state network, underscoring its potential for advanced applications in reservoir computing.