Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices,
and models that contrast the pervasive von Neumann computer architecture. This …

Deep spiking neural networks (SNNs) hold the potential for improving the latency and
energy efficiency of deep neural networks through data-driven event-based computation …

Unsupervised feature extraction algorithms form one of the most important building blocks in
machine learning systems. These algorithms are often adapted to the event-based domain …

Spike-Timing Dependent Plasticity (STDP), the canonical learning rule for spiking neural
networks (SNN), is gaining tremendous interest because of its simplicity, efficiency and …

Neuromorphic devices capable of training spiking neural networks (SNNs) are not easy to
develop due to two main factors: lack of efficient supervised learning algorithms, and high …

Spiking neural networks (SNNs) are more energy-and resource-efficient than artificial neural
networks (ANNs). However, supervised SNN learning is a challenging task due to non …

Earth orbit is a limited natural resource that hosts a vast range of vital space-based systems
that support the international community's national, commercial and defence interests. This …

In this work, we investigate event-based feature extraction through a rigorous framework of
testing. We test a hardware efficient variant of Spike Timing Dependent Plasticity (STDP) on …

We present TinySNN, a novel unsupervised spiking neural network hardware designed for
real-time anomaly detection. TinySNN provides an energy-efficient edge computing solution …

Two-dimensional (2D) MoS2 is regarded as one of the most promising channel materials for
field-effect transistors (FETs) due to its thickness-dependent bandgap and high air-stability …