The backpropagation algorithm has promoted the rapid development of deep learning, but it
relies on a large amount of labeled data and still has a large gap with how humans learn …

Spiking Neural Networks (SNNs) are a pathway that could potentially empower low-power
event-driven neuromorphic hardware due to their spatio-temporal information processing …

Spiking neural networks (SNNs) are bio-inspired neural networks with asynchronous
discrete and sparse characteristics, which have increasingly manifested their superiority in …

Deep spiking neural network (DSNN) is a promising computational model towards artificial
intelligence. It benefits from both the DNNs and SNNs through a hierarchy structure to …

Spiking Neural Networks (SNNs) are considered as the third generation of artificial neural
networks, which are more closely with information processing in biological brains. However …

Spiking neural networks (SNNs) have manifested remarkable advantages in power
consumption and event-driven property during the inference process. To take full advantage …

Abstract Spiking Neural Networks (SNNs) have emerged as a biology-inspired method
mimicking the spiking nature of brain neurons. This bio-mimicry derives SNNs' energy …

Spike Timing Dependent Plasticity (STDP), wherein synaptic weights are modified based on
the temporal correlation between a pair of pre-and post-synaptic (post-neuronal) spikes, is …

Spiking neural networks (SNNs) have made great progress on both performance and
efficiency over the last few years, but their unique working pattern makes it hard to train a …

The problem of training spiking neural networks (SNNs) is a necessary precondition to
understanding computations within the brain, a field still in its infancy. Previous work has …