Continuous-valued deep convolutional networks (DNNs) can be converted into accurate
rate-coding based spike neural networks (SNNs). However, the substantial computational …

Abstract Spiking Neural Networks (SNNs) are promising for enabling low-power event-
driven data analytics. The best performing SNNs for image recognition tasks are obtained by …

Spiking neural networks (SNNs) are promising in a bio-plausible coding for spatio-temporal
information and event-driven signal processing, which is very suited for energy-efficient …

Spiking Neural Networks (SNNs) have emerged as an attractive alternative to traditional
deep learning frameworks, since they provide higher computational efficiency in event …

Spiking neural network (SNN) is promising but the development has fallen far behind
conventional deep neural networks (DNNs) because of difficult training. To resolve the …

Spiking neural networks (SNNs) are considered as one of the most promising artificial
neural networks due to their energy-efficient computing capability. Recently, conversion of a …

Abstract Spiking Neural Networks (SNNs) offer a highly promising computing paradigm due
to their biological plausibility, exceptional spatiotemporal information processing capability …

Deep spiking neural networks (SNNs) support asynchronous event-driven computation,
massive parallelism and demonstrate great potential to improve the energy efficiency of its …

Abstract Spiking Neural Networks (SNNs) offer a promising alternative to traditional deep
learning frameworks, since they provide higher computational efficiency due to event-driven …

Abstract Spiking Neural Networks (SNNs) are considered promising brain-inspired energy-
efficient models due to their event-driven computing paradigm. The spatiotemporal spike …