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 …

Synchronization phenomena in large populations of interacting elements are the subject of
intense research efforts in physical, biological, chemical, and social systems. A successful …

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

In massively parallel computational tasks, such as pattern recognition, conventional
computing architectures have insufficient power efficiency for energy constrained …

Traditional neural network models of associative memories were used to store and retrieve
static patterns. We develop reservoir-computing based memories for complex dynamical …

Networks of coupled periodic oscillators (similar to the Kuramoto model) have been
proposed as models of associative memory. However, error-free retrieval states of such …

When fabricating magnetic memories, one of the main challenges is to maintain the bit
stability while downscaling. Indeed, for magnetic volumes of a few thousand nm3, the …

We propose a novel, brain-inspired deep neural network model known as the Deep
Oscillatory Neural Network (DONN). Deep neural networks like the Recurrent Neural …

We explore a properly interconnected set of Kuramoto type oscillators that results in a new
associative-memory network configuration, which includes second-and third-order …

Neural network devices that inherently possess parallel computing capabilities are generally
difficult to construct because of the large number of neuron–neuron connections. However …