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) operate with asynchronous discrete events (or spikes)
which can potentially lead to higher energy-efficiency in neuromorphic hardware …

Spiking neural networks (SNNs) are positioned to enable spatio-temporal information
processing and ultra-low power event-driven neuromorphic hardware. However, SNNs are …

Spiking Neural Networks (SNNs) have recently emerged as an alternative to deep learning
owing to sparse, asynchronous and binary event (or spike) driven processing, that can yield …

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 …

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 …

Spiking neural networks (SNNs) are well suited for spatio-temporal learning and
implementations on energy-efficient event-driven neuromorphic processors. However …

Spiking Neural Networks (SNNs) are fast becoming a promising candidate for brain-inspired
neuromorphic computing because of their inherent power efficiency and impressive …

Although spiking neural networks (SNNs) take benefits from the bioplausible neural
modeling, the low accuracy under the common local synaptic plasticity learning rules limits …

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 …