Reservoir computing (RC) systems are powerful models for online computations on input
sequences. They consist of a memoryless readout neuron that is trained on top of a …

Randomly connected recurrent neural circuits have proven to be very powerful models for
online computations when a trained memoryless re adout function is appended. Such …

Reservoir Computing is a novel technique which employs recurrent neural networks while
circumventing difficult training algorithms. A very recent trend in Reservoir Computing is the …

Reservoir computing (RC) has attracted a lot of attention in the field of machine learning
because of its promising performance in a broad range of applications. However, it is difficult …

Reservoir Computing (RC) is a paradigm of understanding and training Recurrent Neural
Networks (RNNs) based on treating the recurrent part (the reservoir) differently than the …

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 …

Reservoir computing is a new paradigm for using recurrent neural network with a much
simpler training method. The key idea is to use a large but fixed recurrent part as a reservoir …

To accommodate structured approaches of neural computation, we propose a class of
recurrent neural networks for indexing and storing sequences of symbols or analog data …

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 …

Reservoir Computing (RC) offers a computationally efficient and well performing technique
for using the temporal processing power of Recurrent Neural Networks (RNNs), while …