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Título

Minimal approach to neuro-inspired information processing

AutorSoriano, Miguel C.; Brunner, Daniel ; Escalona-Morán, M. ; Mirasso, Claudio R. ; Fischer, Ingo
Palabras clavePhotonics
Pattern recognition
Reservoir computing
Machine-learning
Information processing: Dynamical systems: Delay
Hardware
Fecha de publicación2-jun-2015
EditorFrontiers Media
CitaciónFrontiers in Computational Neuroscience 9 (2015)
Resumen© 2015 Soriano, Brunner, Escalona-Morán, Mirasso and Fischer. To learn and mimic how the brain processes information has been a major research challenge for decades. Despite the efforts, little is known on how we encode, maintain and retrieve information. One of the hypothesis assumes that transient states are generated in our intricate network of neurons when the brain is stimulated by a sensory input. Based on this idea, powerful computational schemes have been developed. These schemes, known as machine-learning techniques, include artificial neural networks, support vector machine and reservoir computing, among others. In this paper, we concentrate on the reservoir computing (RC) technique using delay-coupled systems. Unlike traditional RC, where the information is processed in large recurrent networks of interconnected artificial neurons, we choose a minimal design, implemented via a simple nonlinear dynamical system subject to a self-feedback loop with delay. This design is not intended to represent an actual brain circuit, but aims at finding the minimum ingredients that allow developing an efficient information processor. This simple scheme not only allows us to address fundamental questions but also permits simple hardware implementations. By reducing the neuro-inspired reservoir computing approach to its bare essentials, we find that nonlinear transient responses of the simple dynamical system enable the processing of information with excellent performance and at unprecedented speed. We specifically explore different hardware implementations and, by that, we learn about the role of nonlinearity, noise, system responses, connectivity structure, and the quality of projection onto the required high-dimensional state space. Besides the relevance for the understanding of basic mechanisms, this scheme opens direct technological opportunities that could not be addressed with previous approaches.
Versión del editorhttp://dx.doi.org/10.3389/fncom.2015.00068
URIhttp://hdl.handle.net/10261/133601
DOI10.3389/fncom.2015.00068
Identificadoresissn: 1662-5188
Aparece en las colecciones: (IFISC) Artículos
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