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 coup
led 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.
The correlated electron system SmNiO3 exhibits a metal-insulator phase transition at 130 {deg}C. Using an ionic liquid as an electric double layer (EDL) gate on three-terminal ultrathin SmNiO3 devices, we investigate gate control of the channel resis
tance and transition temperature. Resistance reduction is observed across both insulating and metallic phases with ~25% modulation at room temperature. We show that resistance modulation is predominantly due to electrostatic charge accumulation and not electrochemical doping by control experiments in inert and air en-vironments. We model the resistance behavior and estimate the accumulated sheet density (~1-2 x 10^14 cm^-2) and EDL capacitance (~12 {mu}F/cm^2).
