Spiking neural networks are being regarded as one of the promising alternative techniques
to overcome the high energy costs of artificial neural networks. It is supported by many …

The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …

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 …

Computation using brain-inspired spiking neural networks (SNNs) with neuromorphic
hardware may offer orders of magnitude higher energy efficiency compared to the current …

Deep neural networks with rate-based neurons have exhibited tremendous progress in the
last decade. However, the same level of progress has not been observed in research on …

The development of spiking neural network simulation software is a critical component
enabling the modeling of neural systems and the development of biologically inspired …

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 …

Recent advances have allowed Deep Spiking Neural Networks (SNNs) to perform at the
same accuracy levels as Artificial Neural Networks (ANNs), but have also highlighted a …

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 …

The Spiking Neural Network (SNN), as one of the biologically inspired neural network
infrastructures, has drawn increasing attention recently. It adopts binary spike activations to …