Reservoir computing is a computational framework suited for temporal/sequential data
processing. It is derived from several recurrent neural network models, including echo state …

Reservoir computing (RC) is a neural computing paradigm especially well-suited for
learning dynamical systems by leveraging an untrained reservoir layer, providing high …

Reservoir computing (RC) is a computational architecture capable of efficiently processing
temporal information, which allows low-cost hardware implementation. However, the …

The connection patterns of neural circuits in the brain form a complex network. Collective
signalling within the network manifests as patterned neural activity and is thought to support …

We study the thermodynamic properties induced by non-reciprocal interactions between
stochastic degrees of freedom in time-and space-continuous systems. We show that, under …

The connection patterns of neural circuits form a complex network. How signaling in these
circuits manifests as complex cognition and adaptive behaviour remains the central question …

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural
network in which neurons are randomly connected. Once initialized, the connection …

In this paper, we investigate the granularity in the free layer of the magnetic tunnel junctions
(MTJ) and its potential to function as a reservoir for reservoir computing where grains act as …

Temporal information processing is critical for a wide spectrum of applications, such as
finance, biomedicine, and engineering. Reservoir computing (RC) can efficiently process …

Deep neural networks (DNNs), a brain-inspired learning methodology, requires tremendous
data for training before performing inference tasks. The recent studies demonstrate a strong …