Reservoir computing using dynamic memristors for temporal information processing

A Comparative Study of Reservoir Computing for Temporal Signal Processing

Reservoir computing (RC) is a novel approach to time series prediction using recurrent neural networks. In RC, an input signal perturbs the intrinsic dynamics of a medium called a reservoir. A readout layer is then trained to reconstruct a target output from the reservoir’s state. The multitude of RC architectures and evaluation metrics poses a challenge to both practitioners and theorists who ...

Visualising Temporal Data Using Reservoir Computing

We create an artificial neural network which is a version of echo state machines, ESNs. ESNs are recurrent neural networks but unlike most recurrent networks, they come with an efficient training method. We adapt this method using ideas from the neuroscale algorithm so that the network is optimal for projecting multivariate time series data onto a low dimensional manifold so that the structure ...

Temporal Pattern Analysis Using Reservoir Computing

On-Line Processing of Grammatical Structure Using Reservoir Computing

Previous words in the sentence can influence the processing of the current word in the timescale of hundreds of milliseconds. The current research provides a possible explanation of how certain aspects of this on-line language processing can occur, based on the dynamics of recurrent cortical networks. We simulate prefrontal area BA47 as a recurrent network that receives on-line input of “gramma...

Toward optical signal processing using photonic reservoir computing.

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 photonic implementation of the recently proposed reservoir computing concept, where the dynamics of a network of nonlinear elements are exploited to perform general signal processing tasks. In our proposed photonic i...