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 …
Direct training high-performance deep spiking neural networks: a review of theories and methods
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 …
neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal …
Training spiking neural networks using lessons from deep learning
The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …
networks. The inner workings of our synapses and neurons provide a glimpse at what the …
Membrane potential batch normalization for spiking neural networks
Y Guo, Y Zhang, Y Chen, W Peng… - Proceedings of the …, 2023 - openaccess.thecvf.com
As one of the energy-efficient alternatives of conventional neural networks (CNNs), spiking
neural networks (SNNs) have gained more and more interest recently. To train the deep …
neural networks (SNNs) have gained more and more interest recently. To train the deep …
Exploring loss functions for time-based training strategy in spiking neural networks
Abstract Spiking Neural Networks (SNNs) are considered promising brain-inspired energy-
efficient models due to their event-driven computing paradigm. The spatiotemporal spike …
efficient models due to their event-driven computing paradigm. The spatiotemporal spike …
Seenn: Towards temporal spiking early exit neural networks
Abstract Spiking Neural Networks (SNNs) have recently become more popular as a
biologically plausible substitute for traditional Artificial Neural Networks (ANNs). SNNs are …
biologically plausible substitute for traditional Artificial Neural Networks (ANNs). SNNs are …
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 …
Eas-snn: End-to-end adaptive sampling and representation for event-based detection with recurrent spiking neural networks
Event cameras, with their high dynamic range and temporal resolution, are ideally suited for
object detection in scenarios with motion blur and challenging lighting conditions. However …
object detection in scenarios with motion blur and challenging lighting conditions. However …
Rate gradient approximation attack threats deep spiking neural networks
Abstract Spiking Neural Networks (SNNs) have attracted significant attention due to their
energy-efficient properties and potential application on neuromorphic hardware. State-of-the …
energy-efficient properties and potential application on neuromorphic hardware. State-of-the …
Parallel spiking neurons with high efficiency and ability to learn long-term dependencies
Vanilla spiking neurons in Spiking Neural Networks (SNNs) use charge-fire-reset neuronal
dynamics, which can only be simulated serially and can hardly learn long-time …
dynamics, which can only be simulated serially and can hardly learn long-time …