Training recurrent neural networks is hard. Recently it has however been discovered that it
is possible to just construct a random recurrent topology, and only train a single linear …

This book sets out to build bridges between the domains of photonic device physics and
neural networks, providing a comprehensive overview of the emerging field of" …

Reservoir computing (RC) refers to a new class of state-space models with a fixed state
transition structure (the reservoir) and an adaptable readout form the state space. The …

Three different uses of a recurrent neural network (RNN) as a reservoir that is not trained but
instead read out by a simple external classification layer have been described in the …

Classification of multivariate time series (MTS) has been tackled with a large variety of
methodologies and applied to a wide range of scenarios. Reservoir computing (RC) …

In this paper, we have described a solution for supporting independent living of the elderly
by means of equipping their home with a simple sensor network to monitor their behaviour …

Activity recognition plays a key role in providing activity assistance and care for users in
smart homes. In this work, we present an activity recognition system that classifies in the …

We propose photonic reservoir computing as a new approach to optical signal processing in
the context of large scale pattern recognition problems. Photonic reservoir computing is a …

The benefits of using intrinsic plasticity (IP), an unsupervised, local, biologically inspired
adaptation rule that tunes the probability density of a neuron's output towards an exponential …

We have combined an echo state network (ESN) with a competitive state machine
framework to create a classification engine called the predictive ESN classifier. We derive …