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

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

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

Event cameras, with their high dynamic range and temporal resolution, are ideally suited for
object detection, especially under scenarios with motion blur and challenging lighting …

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 gained significant attention as a potentially energy-
efficient alternative for standard neural networks with their sparse binary activation …

Spiking Neural Networks (SNNs), as the third generation of neural networks, have gained
prominence for their biological plausibility and computational efficiency, especially in …

In the field of robotics, event-based cameras are emerging as a promising low-power
alternative to traditional frame-based cameras for capturing high-speed motion and high …

Spiking Neural Networks (SNNs) have received considerable attention not only for their
superiority in energy efficiency with discrete signal processing but also for their natural …

Spiking neural networks (SNNs) are gaining popularity in deep learning due to their low
energy budget on neuromorphic hardware. However, they still face challenges in lacking …