Forecasting Events in the Complex Dynamics of a Semiconductor Laser with Optical Feedback

Complex systems performing spiking dynamics are widespread in Nature. They cover from earthquakes, to neurons, variable stars, social networks, or stock markets. Understanding and characterizing their dynamics is relevant in order to detect transitions, or to predict unwanted extreme events. Here we study, under an ordinal patterns analysis, the output intensity of a semiconductor laser with feedback in a regime where it develops a complex spiking behavior. We unveil that, in the transitions towards and from the spiking regime, the complex dynamics presents two competing behaviors that can be distinguished with a thresholding method. Then we use time and intensity correlations to forecast different types of events, and transitions in the dynamics of the system.

[1]  R. Salathé,et al.  External-cavity-induced nonlinearities in the light versus current characteristic of (Ga,Al)As continuous-wave diode lasers , 1977 .

[2]  Y. Kagan,et al.  Earthquakes Cannot Be Predicted , 1997, Science.

[3]  Robert Cedergren,et al.  Guided tour , 1990, Nature.

[4]  Antonio Hurtado,et al.  Investigation of vertical cavity surface emitting laser dynamics for neuromorphic photonic systems , 2012 .

[5]  Maria V. Sanchez-Vives,et al.  Collective stochastic coherence in recurrent neuronal networks , 2016, Nature Physics.

[6]  Bhavin J. Shastri,et al.  Neuromorphic photonic networks using silicon photonic weight banks , 2016, Scientific Reports.

[7]  Jari Saramäki,et al.  Inferring human mobility using communication patterns , 2014, Scientific Reports.

[8]  M. C. Soriano,et al.  Complex photonics: Dynamics and applications of delay-coupled semiconductors lasers , 2013 .

[9]  Max L. Trostel,et al.  Characterizing Complex Dynamics in the Classical and Semi-Classical Duffing Oscillator Using Ordinal Patterns Analysis , 2018, Entropy.

[10]  Y. Kagan Worldwide earthquake forecasts , 2017, Stochastic Environmental Research and Risk Assessment.

[11]  Robert C. Wolpert,et al.  A Review of the , 1985 .

[12]  Jeffrey M. Hausdorff,et al.  Long-range anticorrelations and non-Gaussian behavior of the heartbeat. , 1993, Physical review letters.

[13]  Ellen Zhou,et al.  Neuromorphic photonic networks using silicon photonic weight banks , 2017, Scientific Reports.

[14]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[15]  Cahit Erkal,et al.  Distinguishing between stochastic and deterministic behavior in high frequency foreign exchange rate returns: Can non-linear dynamics help forecasting?☆ , 1996 .

[16]  J. García-Ojalvo,et al.  Effects of noise in excitable systems , 2004 .

[17]  Alvaro Corral Long-term clustering, scaling, and universality in the temporal occurrence of earthquakes. , 2004, Physical review letters.

[18]  Eugene M. Izhikevich,et al.  Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting , 2006 .

[19]  Multidimensional subwavelength position sensing using a semiconductor laser with optical feedback. , 2013, Optics letters.

[20]  Luciano Zunino,et al.  Permutation entropy based time series analysis: Equalities in the input signal can lead to false conclusions , 2017 .

[21]  Niels Wessel,et al.  Classifying cardiac biosignals using ordinal pattern statistics and symbolic dynamics , 2012, Comput. Biol. Medicine.

[22]  Cristina Masoller,et al.  Predictability of extreme intensity pulses in optically injected semiconductor lasers , 2017, 2017 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC).

[23]  Melanie Mitchell,et al.  Complexity - A Guided Tour , 2009 .

[24]  Massimiliano Zanin,et al.  Permutation Entropy and Its Main Biomedical and Econophysics Applications: A Review , 2012, Entropy.

[25]  Paul Charbonneau EPILOGUE:: NATURAL COMPLEXITY , 2017 .

[26]  Cristina Masoller,et al.  Distinguishing signatures of determinism and stochasticity in spiking complex systems , 2013, Scientific Reports.

[27]  Cristina Masoller,et al.  Roadmap on optical rogue waves and extreme events , 2016 .

[28]  James Odell,et al.  Between order and chaos , 2011, Nature Physics.

[29]  J. Danckaert,et al.  Solitary and coupled semiconductor ring lasers as optical spiking neurons. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[30]  P. R. Prucnal,et al.  A Leaky Integrate-and-Fire Laser Neuron for Ultrafast Cognitive Computing , 2013, IEEE Journal of Selected Topics in Quantum Electronics.

[31]  Luciano Zunino,et al.  Forbidden patterns, permutation entropy and stock market inefficiency , 2009 .

[32]  Mariacarla Calzarossa,et al.  Modeling and predicting temporal patterns of web content changes , 2015, J. Netw. Comput. Appl..

[33]  Didier Sornette,et al.  Predictability and suppression of extreme events in a chaotic system. , 2013, Physical review letters.

[34]  Laurent Larger,et al.  Chaos-based communications at high bit rates using commercial fibre-optic links , 2005, Nature.

[35]  Cristina Masoller,et al.  Detecting and quantifying temporal correlations in stochastic resonance via information theory measures , 2009 .

[36]  R. Lang,et al.  External optical feedback effects on semiconductor injection laser properties , 1980 .

[37]  Generation of Rogue Waves in Gyrotrons Operating in the Regime of Developed Turbulence. , 2017, Physical review letters.

[38]  Christoph Bandt,et al.  A New Kind of Permutation Entropy Used to Classify Sleep Stages from Invisible EEG Microstructure , 2017, Entropy.

[39]  M. Freeman,et al.  Natural Complexity , 2008, Science.

[40]  L. Cugliandolo,et al.  Measuring effective temperatures in a generalized Gibbs ensemble. , 2016, Physical review. E.

[41]  Behnam Kia,et al.  Strange nonchaotic stars. , 2015, Physical review letters.

[42]  Cristina Masoller,et al.  Unveiling the complex organization of recurrent patterns in spiking dynamical systems , 2014, Scientific Reports.

[43]  Alexander B Neiman,et al.  Models of stochastic biperiodic oscillations and extended serial correlations in electroreceptors of paddlefish. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[44]  D. Kane,et al.  Variance of permutation entropy and the influence of ordinal pattern selection. , 2017, Physical review. E.

[45]  Daniel Brunner,et al.  Parallel photonic information processing at gigabyte per second data rates using transient states , 2013, Nature Communications.

[46]  H. Wio,et al.  Intermediate valence: Phase diagram and Kondo behaviour in a simple model , 1979 .

[47]  M. Torrent,et al.  Quantitative identification of dynamical transitions in a semiconductor laser with optical feedback , 2016, Scientific Reports.