The field of Deep Learning (DL) has seen a remarkable series of developments with
increasingly accurate and robust algorithms. However, the increase in performance has …

Spiking neural networks (SNNs) serve as a promising computational framework for
integrating insights from the brain into artificial intelligence (AI). Existing software …

The dynamic neural network (DNN), in contrast to the static counterpart, offers numerous
advantages, such as improved accuracy, efficiency, and interpretability. These benefits stem …

The spiking neural network (SNN) mimics the information-processing operation in the
human brain. Directly applying backpropagation to the training of the SNN still has a …

Spiking neural networks (SNNs) are considered an attractive option for edge-side
applications due to their sparse, asynchronous and event-driven characteristics. However …

Spiking neural network (SNN) has attracted much attention due to its powerful spatio-
temporal information representation ability. Capsule Neural Network (CapsNet) does well in …

The non-differentiability of the spike activity has been a hindrance to the development of
high-performance spiking neural networks (SNNs). Current learning algorithms mainly focus …

Neuromorphic object recognition with spiking neural networks (SNNs) is the cornerstone of
low-power neuromorphic computing. However, existing SNNs suffer from significant latency …

Spiking neural network (SNN) has been attached to great importance due to the properties
of high biological plausibility and low energy consumption on neuromorphic hardware. As …

Spiking Neural Networks (SNNs) have attracted significant attention from researchers across
various domains due to their brain-inspired information processing mechanism. However …