Event cameras, with their high dynamic range and temporal resolution, are ideally suited for
object detection in scenarios with motion blur and challenging lighting conditions. However …

Neuromorphic object recognition with spiking neural networks (SNNs) is the cornerstone of
low-power neuromorphic computing. However, existing SNNs suffer from significant latency …

The rapid progress of autonomous driving (AD) has triggered a surge in demand for safer
and more efficient autonomous vehicles, owing to the intricacy of modern urban …

Spike-timing-dependent plasticity (STDP) is an unsupervised learning mechanism for
spiking neural networks that has received significant attention from the neuromorphic …

Complex robotic systems, such as humanoid robot hands, soft robots, and walking robots,
pose a challenging control problem due to their high dimensionality and heavy non …

Spiking Neural Networks (SNNs) have gained increasing attention as energy-efficient neural
networks owing to their binary and asynchronous computation. However, their non-linear …

Spiking Neural Networks (SNNs), providing more realistic neuronal dynamics, have shown
to achieve performance comparable to Artificial Neural Networks (ANNs) in several machine …

Spiking neural networks (SNNs) have received widespread attention as an ultra-low energy
computing paradigm. Recent studies have focused on improving the feature extraction …

The human visual system is a complex and interconnected network comprising billions of
neurons. It plays an essential role in translating environmental light stimuli into information …

In this work, we propose an energy efficient neuromorphic receiver to replace multiple signal-
processing blocks at the receiver by a Spiking Neural Network (SNN) based module, called …