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 …

Deep spiking neural networks (SNNs) hold the potential for improving the latency and
energy efficiency of deep neural networks through data-driven event-based computation …

The spiking neural network (SNN), as a promising brain-inspired computational model with
binary spike information transmission mechanism, rich spatially-temporal dynamics, and …

The brain-inspired and event-driven Spiking Neural Network (SNN) aims at mimicking the
synaptic activity of biological neurons, which transmits binary spike signals between network …

Spiking neural networks (SNNs) transmit information through discrete spikes that perform
well in processing spatial–temporal information. Owing to their nondifferentiable …

Abstract Spiking Neural Networks (SNNs) have attracted enormous research interest due to
temporal information processing capability, low power consumption, and high biological …

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) are promising in ascertaining brain-like behaviors since
spikes are capable of encoding spatio-temporal information. Recent schemes, eg, pre …

Introduction Spiking Neural Networks (SNNs) are gaining significant traction in machine
learning tasks where energy-efficiency is of utmost importance. Training such networks …

The spiking neural network (SNN) is a possible pathway for low-power and energy-efficient
processing and computing exploiting spiking-driven and sparsity features of biological …