Neuromorphic electronics have great potential in the emulation of the sensory, cognitive, self-
learning, and actuating functions of robots. While typically implemented in rigid silicon …

Recently, artificial intelligence (AI)-based algorithms have revolutionized the medical image
segmentation processes. Thus, the precise segmentation of organs and their lesions may …

Spiking Neural Networks are often touted as brain-inspired learning models for the third
wave of Artificial Intelligence. Although recent SNNs trained with supervised …

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 …

A method for controlling the coupling strength is proposed for pulsed coupled active
chemical micro-cells. The method is consistent with Hebb's rules. The effect of various …

Energy and data-efficient online time series prediction for predicting evolving dynamical
systems are critical in several fields, especially edge AI applications that need to update …

Neuromorphic event-based cameras can unlock the true potential of bio-plausible sensing
systems that mimic our human perception. However, efficient spatiotemporal processing …

We aimed to explore the capability of deep learning to approximate the function instantiated
by biological neural circuits-the functional connectome. Using deep neural networks, we …

Spiking Neural Networks (SNNs) have become an essential paradigm in neuroscience and
artificial intelligence, providing brain-inspired computation. Recent advances in literature …