Brain-inspired computing is attracting considerable attention because of its potential to solve
a wide variety of data-intensive problems that are difficult for even state-of-the-art …

Recent advancement of deep learning has been elevated the multifaceted nature in various
applications of this field. Artificial neural networks are now turning into a genuinely old …

This article reviews the recent developments and challenges in spintronic based spiking
neural networks (SNNs). The present CPUs and GPUs are powerful tools that are capable of …

Cognitive networks plan, decide, and act at different layers of the protocol stack, based on
perceived conditions of the network state and assigned rules. We introduce a Cognitive …

The traditional von Neumann architecture seen in digital processors generally suffers from
the data transfer rate bottleneck and inefficiency in terms of energy consumption. The …

The Cognitive Network Controller (CNC) defines a neuromorphic architecture where a
spiking neural network can both encode network performance observations and select the …

Spiking Astrocyte-neuron Networks (SANNs) model the adaptive/repair feature of the human
brain. They integrate astrocyte cells with spiking neurons to facilitate a distributed and fine …

Spiking Neural Networks (SNNs) are often considered the third generation of Artificial
Neural Networks (ANNs), owing to their high information processing capability and the …

Spiking astrocyte neural networks (SANN) are a new computational paradigm that exhibit
enhanced self-adapting and reliability properties. The inclusion of astrocyte behaviour …

We consider a dynamic reservoir of spiking neurons to generate routing decisions for bundle
transmissions in a delay tolerant network (DTN). The reservoir transforms the context, as …