Nonlinear spiking neural P (NSNP) systems are one of neural-like membrane computing
models, abstracted by nonlinear spiking mechanisms of biological neurons. NSNP systems …

Abstract Spiking Neural Networks (SNNs) have recently attracted enormous research
interest since their event-driven and brain-inspired structure enables low-power …

Spiking Neural Networks (SNNs) help achieve brain-like efficiency and functionality by
building neurons and synapses that mimic the human brain's transmission of electrical …

To realize a large-scale Spiking Neural Network (SNN) on hardware for mobile applications,
area and power optimized electronic circuit design is critical. In this work, an area and power …

Current Internet of Things (IoT) embedded applications use machine learning algorithms to
process the collected data. However, the computational complexity and storage …

Explainable recommendations enable users to understand why certain items are suggested
and, ultimately, nurture system transparency, trustworthiness, and confidence. Large …

This paper presents an effective learning multi-spike deep spiking neural network with
temporal feedback backpropagation for breast cancer detection using contrast-enhanced …

Interictal epileptiform spikes (spikes) and epileptogenic focus are strongly correlated.
However, partial spikes are insensitive to epileptogenic focus, which restricts epilepsy …

The synapse is a key element circuit in any memristor-based neuromorphic computing
system. A memristor is a two-terminal analog memory device. Memristive synapses suffer …

In the last few decades, Artificial Neural Networks have become more and more important,
evolving into a powerful tool to implement learning algorithms. Spiking neural networks …