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.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…
cs.NE2024
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…