Distinguishing signatures of determinism and stochasticity in spiking complex systems

Andrés Aragoneses; Nicolás Rubido; Jordi Tiana-Alsina; M.C. Torrent; Cristina Masoller

doi:10.1038/srep01778

Distinguishing signatures of determinism and stochasticity in spiking complex systems

Andrés Aragoneses^* (Corresponding Author), Nicolás Rubido, Jordi Tiana-Alsina, M.C. Torrent, Cristina Masoller

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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Abstract

We describe a method to infer signatures of determinism and stochasticity in the sequence of apparently random intensity dropouts emitted by a semiconductor laser with optical feedback. The method uses ordinal time-series analysis to classify experimental data of inter-dropout-intervals (IDIs) in two categories that display statistically significant different features. Despite the apparent randomness of the dropout events, one IDI category is consistent with waiting times in a resting state until noise triggers a dropout and the other is consistent with dropouts occurring during the return to the resting state, which have a clear deterministic component. The method we describe can be a powerful tool for inferring signatures of determinism in the dynamics of complex systems in noisy environments, at an event-level description of their dynamics.

Original language	English
Article number	1778
Number of pages	6
Journal	Scientific Reports
Volume	3
DOIs	https://doi.org/10.1038/srep01778
Publication status	Published - 7 May 2013

Bibliographical note

Acknowledgements:
This work was supported in part by grant FA8655-12-1-2140 from EOARD US, grant FIS2009-13360 from the Spanish MCI and grant 2009 SGR 1168 from the Generalitat de Catalunya. C. Masoller acknowledges partial support from the ICREA Academia programme. N. Rubido acknowledges the Scottish University Physics Alliance.

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abstract = "We describe a method to infer signatures of determinism and stochasticity in the sequence of apparently random intensity dropouts emitted by a semiconductor laser with optical feedback. The method uses ordinal time-series analysis to classify experimental data of inter-dropout-intervals (IDIs) in two categories that display statistically significant different features. Despite the apparent randomness of the dropout events, one IDI category is consistent with waiting times in a resting state until noise triggers a dropout and the other is consistent with dropouts occurring during the return to the resting state, which have a clear deterministic component. The method we describe can be a powerful tool for inferring signatures of determinism in the dynamics of complex systems in noisy environments, at an event-level description of their dynamics.",

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AU - Masoller, Cristina

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Distinguishing signatures of determinism and stochasticity in spiking complex systems

Abstract

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