Threshold Switching Memristor-Based Spiking Neuron Modeling and Simulation

Spiking neurons are key building blocks in neuromorphic computing for information encoding into spikes. Emergent threshold switching memristors (TSMs) with unique self-rest threshold switching show great promise to facilitate compact spiking neuron designs towards VLSI. In this work, we model the TSMs in standardized Verilog-A and design compact Leaky Integrate-and-Fire (LIF) neurons on Cadence Virtuoso using the Verilog-A model. The neurons are proved capable of processing both digital and analog signals for spike-based neuromorphic computing.