1. Reservoir Computing: Information Processing of Stationary Signals
- Author
-
Juan-Pablo Ortega, Julie Henriques, and Lyudmila Grigoryeva
- Subjects
Series (mathematics) ,Computer science ,Autocorrelation ,Information processing ,Reservoir computing ,Context (language use) ,02 engineering and technology ,Function (mathematics) ,01 natural sciences ,010104 statistics & probability ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,0101 mathematics ,Time series ,Algorithm ,Parametric statistics - Abstract
This paper extends the notion of information processing capacity for non-independent input signals in the context of reservoir computing (RC). The presence of input autocorrelation makes worthwhile the treatment of forecasting and filtering problems for which we explicitly compute this generalized capacity as a function of the reservoir parameter values using a streamlined model. The reservoir model leading to these developments is used to show that, whenever that approximation is valid, this computational paradigm satisfies the so called separation and fading memory properties that are usually associated with good information processing performances. We show that several standard memory, forecasting, and filtering problems that appear in the parametric stochastic time series context can be readily formulated and tackled via RC which, as we show, significantly outperforms standard techniques in some instances.
- Published
- 2016