Neuromorphic computing uses brain-inspired principles to design circuits that can perform
computational tasks with superior power efficiency to conventional computers. Approaches …

Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware
for information processing, capable of highly sophisticated tasks. Systems built with standard …

Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and
machine learning. Recent breakthrough results in image analysis and speech recognition …

It is a fundamental challenge to understand how the function of a network is related to its
structural organization. Adaptive dynamical networks represent a broad class of systems that …

Neurons in the brain behave as nonlinear oscillators, which develop rhythmic activity and
interact to process information. Taking inspiration from this behaviour to realize high-density …

Coupled oscillators are highly complex dynamical systems, and it is an intriguing concept to
use this oscillator dynamics for computation. The idea is not new, but is currently the subject …

Bioinspired hardware holds the promise of low-energy, intelligent, and highly adaptable
computing systems. Applications span from automatic classification for big data …

Collective behavior in large ensembles of dynamical units with nonpairwise interactions may
play an important role in several systems ranging from brain function to social networks …

Many biological and neural systems can be seen as networks of interacting periodic
processes. Importantly, their functionality, ie, whether these networks can perform their …

The discovery of the spin-torque effect has made magnetic nanodevices realistic candidates
for active elements of memory devices and applications. Magnetoresistive effects allow the …