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

Understanding the fundamental relationships between physics and its information-
processing capability has been an active research topic for many years. Physical reservoir …

Reservoir computing is a relatively recent computational paradigm that originates from a
recurrent neural network and is known for its wide range of implementations using different …

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 …

Reservoir Computing is a relatively recent computational framework based on a large
Recurrent Neural Network with fixed weights. Many physical implementations of Reservoir …

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

Reservoir computing is a bioinspired computing paradigm for processing time-dependent
signals. The performance of its analog implementation is comparable to other state-of-the-art …

Reservoir Computing is a class of simple yet efficient Recurrent Neural Networks where
internal weights are fixed at random and only a linear output layer is trained. In the large size …

The echo state network (ESN) is a recently developed machine-learning paradigm whose
processing capabilities rely on the dynamical behavior of recurrent neural networks. Its …

Reservoir Computing is a bio-inspired computing paradigm for processing time dependent
signals that is particularly well suited for analog implementations. Our team has …