Direct learning-based deep spiking neural networks: a review
The spiking neural network (SNN), as a promising brain-inspired computational model with
binary spike information transmission mechanism, rich spatially-temporal dynamics, and …
binary spike information transmission mechanism, rich spatially-temporal dynamics, and …
Spikingjelly: An open-source machine learning infrastructure platform for spike-based intelligence
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 …
chips with high energy efficiency by introducing neural dynamics and spike properties. As …
Neural architecture search for spiking neural networks
Abstract Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-
efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent …
efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent …
Differentiable hierarchical and surrogate gradient search for spiking neural networks
Spiking neural network (SNN) has been viewed as a potential candidate for the next
generation of artificial intelligence with appealing characteristics such as sparse …
generation of artificial intelligence with appealing characteristics such as sparse …
Inherent redundancy in spiking neural networks
Abstract Spiking Neural Networks (SNNs) are well known as a promising energy-efficient
alternative to conventional artificial neural networks. Subject to the preconceived impression …
alternative to conventional artificial neural networks. Subject to the preconceived impression …
Learning rules in spiking neural networks: A survey
Spiking neural networks (SNNs) are a promising energy-efficient alternative to artificial
neural networks (ANNs) due to their rich dynamics, capability to process spatiotemporal …
neural networks (ANNs) due to their rich dynamics, capability to process spatiotemporal …
Towards energy efficient spiking neural networks: An unstructured pruning framework
Spiking Neural Networks (SNNs) have emerged as energy-efficient alternatives to Artificial
Neural Networks (ANNs) when deployed on neuromorphic chips. While recent studies have …
Neural Networks (ANNs) when deployed on neuromorphic chips. While recent studies have …
Auto-spikformer: Spikformer architecture search
Introduction The integration of self-attention mechanisms into Spiking Neural Networks
(SNNs) has garnered considerable interest in the realm of advanced deep learning …
(SNNs) has garnered considerable interest in the realm of advanced deep learning …
Sampling complex topology structures for spiking neural networks
Abstract Spiking Neural Networks (SNNs) have been considered a potential competitor to
Artificial Neural Networks (ANNs) due to their high biological plausibility and energy …
Artificial Neural Networks (ANNs) due to their high biological plausibility and energy …
Convolutional spiking neural networks for spatio-temporal feature extraction
Spiking neural networks (SNNs) can be used in low-power and embedded systems eg
neuromorphic chips due to their event-based nature. They preserve conventional artificial …
neuromorphic chips due to their event-based nature. They preserve conventional artificial …