Neuromorphic learning with Mott insulator NiO
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 16, 2021
- Source ID
- 10.1073/pnas.2017239118
Entities
People
- Alireza Fali
- Fanny Rodolakis
- Hua Zhou
- Hui Cao
- Jason M. Allred
- Jessica L McChesney
- Karin M. Rabe
- Kaushik Roy
- Neda Alsadat Aghamiri
- Qi Wang
- Sandip Mondal
- Shriram Ramanathan
- Subhasish Mandal
- Yifei Sun
- Yohannes Abate
- Zhan Zhang
- Zhen Zhang
Organizations
- Argonne National Laboratory
- Purdue University
- Rutgers University
- University of Georgia