Emerged as a biology-inspired method, Spiking Neural Networks (SNNs) mimic the spiking
nature of brain neurons and have received lots of research attention. SNNs deal with binary …

Spiking neural networks (SNNs) based on the leaky integrate and fire (LIF) model have
been applied to energy-efficient temporal and spatiotemporal processing tasks. Due to the …

Spiking neural networks (SNNs) are a promising energy-efficient alternative to artificial
neural networks (ANNs) due to their rich dynamics, capability to process spatiotemporal …

Spiking neural networks are known to be superior over artificial neural networks for their
computational power efficiency and noise robustness. The benefits of spiking coupled with …

Spiking neural networks (SNNs) have shown much promise as an energy-efficient
alternative to artificial neural networks (ANNs). Such methods trained by surrogate gradient …

Medical image registration is an important task in automated analysis of multimodal images
and temporal data involving multiple patient visits. Conventional approaches, although …

Finding spike-based learning algorithms that can be implemented within the local
constraints of neuromorphic systems, while achieving high accuracy, remains a formidable …

Spiking neural networks (SNNs) are brain-inspired machine learning algorithms with merits
such as biological plausibility and unsupervised learning capability. Previous works have …

Neuromorphic computing is becoming a popular approach for implementations of brain-
inspired machine learning tasks. As a paradigm for both hardware and algorithm design …

The bio-inspired concept of Spike-Timing-Dependent Plasticity (STDP) derived from
neurobiology is increasingly used in Spiking Neural Networks (SNNs) nowadays. Mostly …