Experimentally Validated Pr0.7Ca0.3MnO3 RRAM Verilog-A model based Izhikevich Neuronal Dynamics

Spiking Neural Networks (SNNs) are brain-inspired computational networks that promise an efficient solution for reallife applications such as audio processing and pattern recognition. An SNN is a complex network of neurons interconnected with synapses, whose spike times and synaptic strength is essential for information processing. SNNs consume low power and perform computations in parallel, making them attractive for hardware implementation of applications in neuromorphic engineering. For hardware implementation of SNNs, we need devices that mimic the behavior of neurons and synapses as well as their computational models to build and characterize large-scale SNNs. Earlier, a Pr0.7Ca0.3MnO3 (PCMO) material based Resistive-RAM (RRAM) has been used to experimentally demonstrate an Integrate and Fire (IF) Neuron and Izhikevich Neuron. Here, we present an experimentally validated Verilog-A model of PCMO RRAM, which behaves like a neuron. The model captures the conductance change of the RRAM for different applied voltages and can mimic an IF Neuron and an Izhikevich Neuron. The model enables the design of large-scale SNNs and studies their behavior in a simulation domain.