To realize highly efficient neuromorphic computing that is comparable to biological
counterparts, bioinspired computing systems, consisting of biorealistic artificial synapses …

Spintronics is a growing research field that focuses on exploring materials and devices that
take advantage of the electron's “spin” to go beyond charge based devices. The most …

The modelling of large systems of spiking neurons is computationally very demanding in
terms of processing power and communication. SpiNNaker-Spiking Neural Network …

Learning in multilayer neural networks (MNNs) relies on continuous updating of large
matrices of synaptic weights by local rules. Such locality can be exploited for massive …

Back propagation (BP) based on stochastic gradient descent is the prevailing method to
train multilayer neural networks (MNNs) with hidden layers. However, the existence of the …

We propose a design methodology to exploit adaptive nanodevices (memristors), virtually
immune to their variability. Memristors are used as synapses in a spiking neural network …

This paper presents new theoretical results on the invariance and attractivity of memristor-
based cellular neural networks (MCNNs) with time-varying delays. First, sufficient conditions …

Cognitive tasks are essential for the modern applications of electronics, and rely on the
capability to perform inference. The Von Neumann bottleneck is an important issue for such …

A compact neuromorphic nanodevice with inherent learning and memory properties
emulating those of biological synapses is the key to developing artificial neural networks …

In order to map the computing architecture and intelligent functions of the human brain on
hardware, we need electronic devices that can emulate biological synapses and even …