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 …

Artificial neural networks (ANNs), a popular path towards artificial intelligence, have
experienced remarkable success via mature models, various benchmarks, open-source …

Abstract Spiking Neural Networks (SNNs) have recently emerged as a new generation of
low-power deep neural networks due to sparse, asynchronous, and binary event-driven …

We propose a spiking neural network model that encodes information in the relative timing
of individual neuron spikes and performs classification using the first output neuron to spike …

Abstract Spiking Neural Networks (SNNs) have been attached great importance due to the
distinctive properties of low power consumption, biological plausibility, and adversarial …

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

Neuromorphic computing is henceforth a major research field for both academic and
industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at …

Bio-inspired spiking neural networks (SNNs), operating with asynchronous binary signals
(or spikes) distributed over time, can potentially lead to greater computational efficiency on …

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 …

Neuromorphic vision sensor is a new bio-inspired imaging paradigm that reports
asynchronous, continuously per-pixel brightness changes called'events' with high temporal …