Approaching limitations of digital computing technologies have spurred research in
neuromorphic and other unconventional approaches to computing. Here we argue that if we …

Accurate detection of pathological conditions in human subjects can be achieved through off-
line analysis of recorded biological signals such as electrocardiograms (ECGs). However …

The accelerating race of digital computing technologies seems to be steering towards
impasses—technological, economical and environmental—a condition that has spurred …

In the brain, information is encoded, transmitted and used to inform behaviour at the level of
timing of action potentials distributed over population of neurons. To implement neural-like …

Abstract From one side, Evolutionary Algorithms have enabled enormous progress over the
last years in the optimization field. They have been applied to a variety of problems …

Deep neural networks have dramatically transformed machine learning, but their memory
and energy demands are substantial. The requirements of real biological neural networks …

Transistor-based chaotic oscillators are known to realize highly diverse dynamics despite
having elementary circuit topologies. This work investigates, numerically and experimentally …

The rapidly growing computational demands of deep neural networks require novel
hardware designs. Recently, tuneable nanoelectronic devices were developed based on …

This article is a public deliverable of the EU project" Memory technologies with multi-scale
time constants for neuromorphic architectures"(MeMScales, https://memscales. eu, Call ICT …

The acceleration race of digital computing technologies seems to be steering toward
impasses--technological, economical and environmental--a condition that has spurred …