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 …

Spiking neural networks (SNNs) use spatiotemporal spike patterns to represent and transmit
information, which are not only biologically realistic but also suitable for ultralow-power …

In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …

Spiking neural networks (SNNs) are nature's versatile solution to fault-tolerant, energy-
efficient signal processing. To translate these benefits into hardware, a growing number of …

Abstract Spiking Neural Networks (SNNs) are promising energy-efficient models for
neuromorphic computing. For training the non-differentiable SNN models, the …

Spiking neural networks (SNNs) that mimic information transmission in the brain can energy-
efficiently process spatio-temporal information through discrete and sparse spikes, thereby …

Spiking neural networks (SNNs) with biologically inspired spatio-temporal dynamics
demonstrate superior energy efficiency on neuromorphic architectures. Error …

Spiking neural networks (SNNs) are shown to be more biologically plausible and energy
efficient over their predecessors. However, there is a lack of an efficient and generalized …

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) represent the most prominent biologically inspired
computing model for neuromorphic computing (NC) architectures. However, due to the …