Neuromimetic Circuits with Synaptic Devices based on Strongly Correlated Electron Systems

TL;DR

This paper demonstrates neuromimetic circuits using nickelate-based synaptic devices that emulate biological learning and unlearning, offering insights into neural processes and enabling advanced hardware-based information processing.

Contribution

It introduces a physical model for nickelate synaptic devices and demonstrates real-time learning, unlearning, and neural network simulations in hardware.

Findings

Successful real-time classical conditioning and unlearning in circuits

Development of a physical model based on ionic-electronic diffusion

Simulation of associative learning and memory storage in hardware

Abstract

Strongly correlated electron systems such as the rare-earth nickelates (RNiO3, R = rare-earth element) can exhibit synapse-like continuous long term potentiation and depression when gated with ionic liquids; exploiting the extreme sensitivity of coupled charge, spin, orbital, and lattice degrees of freedom to stoichiometry. We present experimental real-time, device-level classical conditioning and unlearning using nickelate-based synaptic devices in an electronic circuit compatible with both excitatory and inhibitory neurons. We establish a physical model for the device behavior based on electric-field driven coupled ionic-electronic diffusion that can be utilized for design of more complex systems. We use the model to simulate a variety of associate and non-associative learning mechanisms, as well as a feedforward recurrent network for storing memory. Our circuit intuitively parallels biological neural architectures, and it can be readily generalized to other forms of cellular learning and extinction. The simulation of neural function with electronic device analogues may provide insight into biological processes such as decision making, learning and adaptation, while facilitating advanced parallel information processing in hardware.