Recently, both industry and academia have proposed several different neuromorphic
systems to execute machine learning applications that are designed using Spiking Neural …

A sustainable computing scenario demands more energy-efficient processors.
Neuromorphic systems mimic biological functions by employing spiking neural networks for …

The effectiveness, robustness, and flexibility of memory and learning constitute the very
essence of human natural intelligence, cognition, and consciousness. However, currently …

Neuromorphic Systems-on-Chip (NSoCs) are becoming heterogeneous by integrating
general-purpose processors (GPPs) and neural processing units (NPUs) on the same SoC …

Medical data mainly includes various types of biomedical signals and medical images,
which can be used by professional doctors to make judgments on patients' health …

Survival analysis aims to analyze the relationship between covariates and events of interest,
and is widely applied in multiple research fields, especially in clinical fields. Recently, some …

Body area networks (BANs) play a pivotal role in modern healthcare, enabling real‐time
data collection and monitoring of vital patient parameters, thereby empowering healthcare …

Fractional calculus is an emerging topic in artificial neural network training, especially when
using gradient-based methods. This paper brings the idea of fractional derivatives to spiking …

The development of a robust anomaly detection system capable of distinguishing between
normal and abnormal activities in paediatric care settings is of paramount importance for …

Neuromorphic systems are integrated circuits designed to mimic the event-driven
computations in a mammalian brain. These systems enable the execution of machine …