Recent computational models based on reservoir com-puting (RC) are gaining attention as plausible theories of cortical information processing. In these models, the activity of a recurrently connected population of neurons is sent to one or many read-out units through a linear transformation. These models can operate in a chaotic regime which has been proposed as a possible mechan-ism underlying sustained irregular activity observed i
Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network...
Networks of living neurons exhibit diverse patterns of activity, including oscillations, synchrony, ...
Reservoir computing (RC) is a powerful computational paradigm that allows high versatility with chea...
Chaos in dynamical systems potentially provides many different dynamical states arising from a singl...
Dynamical systems suited for Reservoir Computing (RC) should be able to both retain information for ...
Reservoir computing (RC) studies the properties of large recurrent networks of artificial neurons, w...
The remarkable properties of information-processing by biological and artificial neuronal networks a...
Reservoir computing (RC) systems are powerful models for online computations on input sequences. The...
Reservoir computing (RC) is a brain-inspired computing framework that employs a transient dynamical ...
Reservoir computing (RC) is a promising paradigm for time series processing. In this paradigm, the d...
Abstract. It has been proposed that chaos can serve as a reservoir providing an infinite number of d...
[eng] Physical dynamical systems are able to process information in a nontrivial manner. The machin...
We investigate information processing in randomly connected recurrent neural networks. It has been s...
My study is founded on recurrent neural networks but using RC method leads us to a faster process wi...
Physical dynamical systems are able to process information in a nontrivial manner. The machine learn...
Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network...
Networks of living neurons exhibit diverse patterns of activity, including oscillations, synchrony, ...
Reservoir computing (RC) is a powerful computational paradigm that allows high versatility with chea...
Chaos in dynamical systems potentially provides many different dynamical states arising from a singl...
Dynamical systems suited for Reservoir Computing (RC) should be able to both retain information for ...
Reservoir computing (RC) studies the properties of large recurrent networks of artificial neurons, w...
The remarkable properties of information-processing by biological and artificial neuronal networks a...
Reservoir computing (RC) systems are powerful models for online computations on input sequences. The...
Reservoir computing (RC) is a brain-inspired computing framework that employs a transient dynamical ...
Reservoir computing (RC) is a promising paradigm for time series processing. In this paradigm, the d...
Abstract. It has been proposed that chaos can serve as a reservoir providing an infinite number of d...
[eng] Physical dynamical systems are able to process information in a nontrivial manner. The machin...
We investigate information processing in randomly connected recurrent neural networks. It has been s...
My study is founded on recurrent neural networks but using RC method leads us to a faster process wi...
Physical dynamical systems are able to process information in a nontrivial manner. The machine learn...
Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network...
Networks of living neurons exhibit diverse patterns of activity, including oscillations, synchrony, ...
Reservoir computing (RC) is a powerful computational paradigm that allows high versatility with chea...