Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As …
Neuromorphic vision sensor is a new bio-inspired imaging paradigm that reports asynchronous, continuously per-pixel brightness changes called'events' with high temporal …
K Che, L Leng, K Zhang, J Zhang… - Advances in …, 2022 - proceedings.neurips.cc
Spiking neural network (SNN) has been viewed as a potential candidate for the next generation of artificial intelligence with appealing characteristics such as sparse …
Y Sakemi, K Yamamoto, T Hosomi, K Aihara - Scientific Reports, 2023 - nature.com
The training of multilayer spiking neural networks (SNNs) using the error backpropagation algorithm has made significant progress in recent years. Among the various training …
C Zhou, H Zhang, L Yu, Y Ye, Z Zhou… - Frontiers in …, 2024 - frontiersin.org
Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal …
Spiking neural networks (SNN), also often referred to as the third generation of neural networks, carry the potential for a massive reduction in memory and energy consumption …
Event-based cameras are raising interest within the computer vision community. These sensors operate with asynchronous pixels, emitting events, or “spikes”, when the luminance …
Spiking neural networks (SNNs) have shown much promise as an energy-efficient alternative to artificial neural networks (ANNs). Such methods trained by surrogate gradient …
S Lucas, E Portillo - Neural Networks, 2024 - Elsevier
Abstract Spiking Neural Networks (SNN) are recognised as well-suited for processing spatiotemporal information with ultra-low energy consumption. However, proposals based …