Adaptive memristor-based LIF neuron circuit for energy efficient SNN crossbar array

Spiking neural networks (SNN) provide superior potential for neuromorphic architecture implementation due to its similarity to biological brain structures and exceptional computing efficiency. Neurons are the fundamental elements of SNN, and the incorporation of frequency adaptation enhances the performance of SNN significantly. The study implements an adaptive leaky-integrate-and-fire (LIF) neuron utilizing volatile and non-volatile memristors. The design offers control of adaptive response via inter-pulse interval of the input and certain circuit characteristics, including membrane capacitance and the initial resistance state of the volatile memristor. This work presents a novel SNN crossbar circuit of dimensions 2x2 and 5x5, offering several advantages including bio-realistic spike generation, reduce energy per spike consumption, no requirement of additional neuron reset circuitry, improving scalability and integration. The Cadence Virtuoso 180 nm simulation environment has been utilized to demonstrate firing dynamics of the adaptive SNN. The study emphasizes potential of adaptive spiking neural network circuits in facilitating efficient neuromorphic applications in forthcoming research.