Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial
neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal …

Video action recognition (VAR) plays crucial roles in various domains such as surveillance,
healthcare, and industrial automation, making it highly significant for the society …

This paper shows that the heterogeneity in neuronal and synaptic dynamics reduces the
spiking activity of a Recurrent Spiking Neural Network (RSNN) while improving prediction …

Spiking Neural Networks (SNNs) represent the forefront of neuromorphic computing,
promising energy-efficient and biologically plausible models for complex tasks. This paper …

The natural evolution of the human brain has given rise to multiple forms of synaptic
plasticity, allowing for dynamic changes to adapt to an ever-evolving world. The evolutionary …

Rapid advancements in 5G communication and the Internet-of-things have prompted the
development of cognitive radio sensing for spectrum monitoring and malicious attack …

Cortical networks are capable of unsupervised learning and spontaneous replay of complex
temporal sequences. Endowing artificial spiking neural networks with similar learning …

Human action recognition (HAR) is a rapidly growing field with numerous applications in
various domains. HAR involves the development of algorithms and techniques to …

Surface electromyography (sEMG) based gesture recognition shows promise in enhancing
human-robot interaction. However, accurately recognizing similar gestures is a challenging …

Spiking neural network (SNN) distinguish themselves from artificial neural network (ANN)
because of their inherent temporal processing and spike-based computations, enabling a …