3 papers
cs.NE2025
Revisiting Direct Encoding: Learnable Temporal Dynamics for Static Image Spiking Neural Networks
Huaxu He
Handling static images that lack inherent temporal dynamics remains a fundamental challenge for spiking neural networks (SNNs). In directly trained SNNs, static inputs are typicall…
cs.NE2025
Optimizing Spatio-Temporal Information Processing in Spiking Neural Networks via Unconstrained Leaky Integrate-and-Fire Neurons and Hybrid Coding
Huaxu He
Spiking Neural Networks (SNN) exhibit higher energy efficiency compared to Artificial Neural Networks (ANN) due to their unique spike-driven mechanism. Additionally, SNN possess a…
cs.AI2024
Enhanced Temporal Processing in Spiking Neural Networks for Static Object Detection Using 3D Convolutions
Huaxu He
Spiking Neural Networks (SNNs) are a class of network models capable of processing spatiotemporal information, with event-driven characteristics and energy efficiency advantages. R…