Artificial neural networks (ANNs) have experienced a rapid advancement for their success in
various application domains, including autonomous driving and drone vision. Researchers …

Abstract Spiking Neural Networks (SNNs) are promising in neuromorphic hardware owing to
utilizing spatio-temporal information and sparse event-driven signal processing. However, it …

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 …

Abstract Spiking Neural Networks (SNNs) have recently emerged as a new generation of
low-power deep neural networks due to sparse, asynchronous, and binary event-driven …

Spiking neural networks (SNNs) are promising to be widely deployed in real-time and safety-
critical applications with the advance of neuromorphic computing. Recent work has …

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

In medicine, white blood cells (WBCs) play an important role in the human immune system.
The different types of WBC abnormalities are related to different diseases so that the total …

Thanks to their event-driven nature, spiking neural networks (SNNs) are surmised to be
great computation-efficient models. The spiking neurons encode beneficial temporal facts …

Spiking neural networks (SNNs) serve as one type of efficient model to process spatio-
temporal patterns in time series, such as the Address-Event Representation data collected …

Neural coding, including encoding and decoding, is one of the key problems in
neuroscience for understanding how the brain uses neural signals to relate sensory …