Biologically inspired spiking neural networks are increasingly popular in the field of artificial
intelligence due to their ability to solve complex problems while being power efficient. They …

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

Spiking neural networks (SNNs) can potentially offer an efficient way of doing inference
because the neurons in the networks are sparsely activated and computations are event …

Abstract Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-
efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent …

Spiking neural networks are the basis of versatile and power-efficient information processing
in the brain. Although we currently lack a detailed understanding of how these networks …

Spiking neural networks (SNNs) represent the most prominent biologically inspired
computing model for neuromorphic computing (NC) architectures. However, due to the …

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

The activations of an analog neural network (ANN) are usually treated as representing an
analog firing rate. When mapping the ANN onto an equivalent spiking neural network (SNN) …

The combination of spiking neural networks and event-based vision sensors holds the
potential of highly efficient and high-bandwidth optical flow estimation. This paper presents …

Neuromorphic hardware strives to emulate brain-like neural networks and thus holds the
promise for scalable, low-power information processing on temporal data streams. Yet, to …