Adaptive stochastic resonance for unknown and variable input signals

Abstract All sensors have a threshold, defined by the smallest signal amplitude that can be detected. The detection of sub-threshold signals, however, is possible by using the principle of stochastic resonance, where noise is added to the input signal so that it randomly exceeds the sensor threshold...

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Autores principales:	Patrick Krauss, Claus Metzner, Achim Schilling, Christian Schütz, Konstantin Tziridis, Ben Fabry, Holger Schulze
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f136b21fce664103affdd5c445506309
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spelling	oai:doaj.org-article:f136b21fce664103affdd5c4455063092021-12-02T16:06:48ZAdaptive stochastic resonance for unknown and variable input signals10.1038/s41598-017-02644-w2045-2322https://doaj.org/article/f136b21fce664103affdd5c4455063092017-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-02644-whttps://doaj.org/toc/2045-2322Abstract All sensors have a threshold, defined by the smallest signal amplitude that can be detected. The detection of sub-threshold signals, however, is possible by using the principle of stochastic resonance, where noise is added to the input signal so that it randomly exceeds the sensor threshold. The choice of an optimal noise level that maximizes the mutual information between sensor input and output, however, requires knowledge of the input signal, which is not available in most practical applications. Here we demonstrate that the autocorrelation of the sensor output alone is sufficient to find this optimal noise level. Furthermore, we demonstrate numerically and analytically the equivalence of the traditional mutual information approach and our autocorrelation approach for a range of model systems. We furthermore show how the level of added noise can be continuously adapted even to highly variable, unknown input signals via a feedback loop. Finally, we present evidence that adaptive stochastic resonance based on the autocorrelation of the sensor output may be a fundamental principle in neuronal systems.Patrick KraussClaus MetznerAchim SchillingChristian SchützKonstantin TziridisBen FabryHolger SchulzeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-8 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Patrick Krauss Claus Metzner Achim Schilling Christian Schütz Konstantin Tziridis Ben Fabry Holger Schulze Adaptive stochastic resonance for unknown and variable input signals
description	Abstract All sensors have a threshold, defined by the smallest signal amplitude that can be detected. The detection of sub-threshold signals, however, is possible by using the principle of stochastic resonance, where noise is added to the input signal so that it randomly exceeds the sensor threshold. The choice of an optimal noise level that maximizes the mutual information between sensor input and output, however, requires knowledge of the input signal, which is not available in most practical applications. Here we demonstrate that the autocorrelation of the sensor output alone is sufficient to find this optimal noise level. Furthermore, we demonstrate numerically and analytically the equivalence of the traditional mutual information approach and our autocorrelation approach for a range of model systems. We furthermore show how the level of added noise can be continuously adapted even to highly variable, unknown input signals via a feedback loop. Finally, we present evidence that adaptive stochastic resonance based on the autocorrelation of the sensor output may be a fundamental principle in neuronal systems.
format	article
author	Patrick Krauss Claus Metzner Achim Schilling Christian Schütz Konstantin Tziridis Ben Fabry Holger Schulze
author_facet	Patrick Krauss Claus Metzner Achim Schilling Christian Schütz Konstantin Tziridis Ben Fabry Holger Schulze
author_sort	Patrick Krauss
title	Adaptive stochastic resonance for unknown and variable input signals
title_short	Adaptive stochastic resonance for unknown and variable input signals
title_full	Adaptive stochastic resonance for unknown and variable input signals
title_fullStr	Adaptive stochastic resonance for unknown and variable input signals
title_full_unstemmed	Adaptive stochastic resonance for unknown and variable input signals
title_sort	adaptive stochastic resonance for unknown and variable input signals
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/f136b21fce664103affdd5c445506309
work_keys_str_mv	AT patrickkrauss adaptivestochasticresonanceforunknownandvariableinputsignals AT clausmetzner adaptivestochasticresonanceforunknownandvariableinputsignals AT achimschilling adaptivestochasticresonanceforunknownandvariableinputsignals AT christianschutz adaptivestochasticresonanceforunknownandvariableinputsignals AT konstantintziridis adaptivestochasticresonanceforunknownandvariableinputsignals AT benfabry adaptivestochasticresonanceforunknownandvariableinputsignals AT holgerschulze adaptivestochasticresonanceforunknownandvariableinputsignals
_version_	1718384878110638080

Adaptive stochastic resonance for unknown and variable input signals

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