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Strongly correlated electron systems such as the rare-earth nickelates(RNiO3, R = rare-earth element) can exhibit synapse-like continuous long termpotentiation and depression when gated with ionic liquids; exploiting theextreme sensitivity of coupled charge, spin, orbital, and lattice degrees offreedom to stoichiometry. We present experimental real-time, device-levelclassical conditioning and unlearning using nickelate-based synaptic devices inan electronic circuit compatible with both excitatory and inhibitory neurons.We establish a physical model for the device behavior based on electric-fielddriven coupled ionic-electronic diffusion that can be utilized for design ofmore complex systems. We use the model to simulate a variety of associate andnon-associative learning mechanisms, as well as a feedforward recurrent networkfor storing memory. Our circuit intuitively parallels biological neuralarchitectures, and it can be readily generalized to other forms of cellularlearning and extinction. The simulation of neural function with electronicdevice analogues may provide insight into biological processes such as decisionmaking, learning and adaptation, while facilitating advanced parallelinformation processing in hardware.

Neuromimetic Circuits with Synaptic Devices based on Strongly Correlated Electron Systems