Neuromimetic circuits with synaptic devices based on strongly correlated electron systems

Sieu D. Ha, Jian Shi, Yasmine Meroz, L. Mahadevan, Shriram Ramanathan

Research output: Contribution to journalArticlepeer-review


Strongly correlated electron systems such as the rare-earth nickelates (RNiO3, R denotes a rare-earth element) can exhibit synapselike 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 nonassociative 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 analogs may provide insight into biological processes such as decision making, learning, and adaptation, while facilitating advanced parallel information processing in hardware.

Original languageEnglish
Article number064003
JournalPhysical Review Applied
Issue number6
StatePublished - 4 Dec 2014
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Physics and Astronomy


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