Year: 2025
Type: conference-paper
Abstract: Over the past few years, Spiking Neural Networks (SNNs), acknowledged as the third generation of neural networks, have garnered substantial research attention owing to their distinctive temporal dynamics, diverse encoding mechanisms, and event-driven nature, which fundamentally differentiate them from conventional artificial neural networks. In this paper, we improve the RLIF neuron model, propose a circuit structure that emphasizes the refractory period characteristics of neurons, and complete the circuit design based on 180nmCMOS technology. The circuit can be used to study the dynamic behavior of SNN neural networks, and can be used as the basic unit of integrated analog pulse neural network circuits and systems.
Authors: Huiwen Lu, Jin Li, Jiuqi Yin, Xingwen Li
Institution: Hubei University of Technology
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410813846