Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead
of capturing images at a fixed rate, they asynchronously measure per-pixel brightness …

Spiking neural networks (SNNs) are inspired by information processing in biology, where
sparse and asynchronous binary signals are communicated and processed in a massively …

Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …

Neuromorphic event-based image sensors capture only the dynamic motion in a scene,
which is then transferred to computation units for motion recognition. This approach …

Since the beginning of information processing by electronic components, the nervous
system has served as a metaphor for the organization of computational primitives. Brain …

Spiking Neural Networks (SNNs) have recently emerged as a prominent neural computing
paradigm. However, the typical shallow SNN architectures have limited capacity for …

Recurrent networks of spiking neurons (RSNNs) underlie the astounding computing and
learning capabilities of the brain. But computing and learning capabilities of RSNN models …

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 …

A graphdiyne-based artificial synapse (GAS), exhibiting intrinsic short-term plasticity, has
been proposed to mimic biological signal transmission behavior. The impulse response of …

Abstract Spiking Neural Networks (SNNs) have received extensive academic attention due
to the unique properties of low power consumption and high-speed computing on …