Neuromorphic learning with Mott insulator NiO

Abstract

Significance Neuromorphic computing requires emulation of animal learning in synthetic matter. Materials with highly tunable electronic structures and a dynamical response to environmental stimuli are particularly suited for this task. Here, we demonstrate universal learning characteristics such as habituation and sensitization in a prototypical quantum material, NiO. With stimuli such as oxygen, ozone, and light, the concentration of atomic defects can be modulated reversibly, resulting in changes to electrical conductivity that mimic nonassociative learning. The material behavior inspires new algorithms for unsupervised learning in neural networks and opens up new directions for use of Mott insulators in artificial intelligence. Habituation and sensitization (nonassociative learning) are among the most fundamental forms of learning and memory behavior present in organisms that enable adaptation and learning in dynamic environments. Emulating such features of intelligence found in nature in the solid state can serve as inspiration for algorithmic simulations in artificial neural networks and potential use in neuromorphic computing. Here, we demonstrate nonassociative learning with a prototypical Mott insulator, nickel oxide (NiO), under a variety of external stimuli at and above room temperature. Similar to biological species such as Aplysia, habituation and sensitization of NiO possess time-dependent plasticity relying on both strength and time interval between stimuli. A combination of experimental approaches and first-principles calculations reveals that such learning behavior of NiO results from dynamic modulation of its defect and electronic structure. An artificial neural network model inspired by such nonassociative learning is simulated to show advantages for an unsupervised clustering task in accuracy and reducing catastrophic interference, which could help mitigate the stability–plasticity dilemma. Mott insulators can therefore serve as building blocks to examine learning behavior noted in biology and inspire new learning algorithms for artificial intelligence.