The reservoir's perspective on generalized synchronization.

We employ reservoir computing for a reconstruction task in coupled chaotic systems, across a range of dynamical relationships including generalized synchronization. For a drive-response setup, a temporal representation of the synchronized state is discussed as an alternative to the known instantaneous form. The reservoir has access to both representations through its fading memory property, each with advantages in different dynamical regimes. We also extract signatures of the maximal conditional Lyapunov exponent in the performance of variations of the reservoir topology. Moreover, the reservoir model reproduces different levels of consistency where there is no synchronization. In a bidirectional coupling setup, high reconstruction accuracy is achieved despite poor observability and independent of generalized synchronization.

[1]  L. A. Aguirre,et al.  Investigating observability properties from data in nonlinear dynamics. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  Laurent Larger,et al.  High-Speed Photonic Reservoir Computing Using a Time-Delay-Based Architecture: Million Words per Second Classification , 2017 .

[3]  Herbert Jaeger,et al.  Reservoir computing approaches to recurrent neural network training , 2009, Comput. Sci. Rev..

[4]  S. Boccaletti,et al.  Synchronization of chaotic systems , 2001 .

[5]  Philip T. Clemson,et al.  Discerning non-autonomous dynamics , 2014 .

[6]  Benjamin Schrauwen,et al.  Information Processing Capacity of Dynamical Systems , 2012, Scientific Reports.

[7]  Jaideep Pathak,et al.  Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data. , 2017, Chaos.

[8]  Ulrich Parlitz Detecting generalized synchronization , 2012 .

[9]  T L Carroll,et al.  Testing dynamical system variables for reconstruction. , 2018, Chaos.

[10]  Antonio Politi,et al.  Characterizing the response of chaotic systems. , 2010, Physical review letters.

[11]  Michael Small,et al.  Consistency in echo-state networks. , 2019, Chaos.

[12]  Ingo Fischer,et al.  Limits to detection of generalized synchronization in delay-coupled chaotic oscillators. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Robert Haslinger,et al.  Statistical modeling approach for detecting generalized synchronization. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  PAUL J. WERBOS,et al.  Generalization of backpropagation with application to a recurrent gas market model , 1988, Neural Networks.

[15]  Henry Markram,et al.  Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.

[16]  VerstraetenD.,et al.  2007 Special Issue , 2007 .

[17]  Atsushi Uchida,et al.  Laser dynamical reservoir computing with consistency: an approach of a chaos mask signal. , 2016, Optics express.

[18]  Ulrich Parlitz,et al.  Observing spatio-temporal dynamics of excitable media using reservoir computing. , 2018, Chaos.

[19]  Wolfgang Kinzel,et al.  Strong and weak chaos in networks of semiconductor lasers with time-delayed couplings. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Benjamin Schrauwen,et al.  Reservoir Computing Trends , 2012, KI - Künstliche Intelligenz.

[21]  L. Appeltant,et al.  Information processing using a single dynamical node as complex system , 2011, Nature communications.

[22]  Arkady Pikovsky,et al.  Synchronization and desynchronization of self-sustained oscillators by common noise. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  Stefano Boccaletti,et al.  Generalized synchronization in mutually coupled oscillators and complex networks. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  L Pesquera,et al.  Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing. , 2012, Optics express.

[25]  W. Kinzel,et al.  Strong and weak chaos in nonlinear networks with time-delayed couplings. , 2011, Physical review letters.

[26]  M. C. Soriano,et al.  A Unified Framework for Reservoir Computing and Extreme Learning Machines based on a Single Time-delayed Neuron , 2015, Scientific Reports.

[27]  L. Glass,et al.  Oscillation and chaos in physiological control systems. , 1977, Science.

[28]  T. Sejnowski,et al.  Reliability of spike timing in neocortical neurons. , 1995, Science.

[29]  Alexey A. Koronovskii,et al.  Intermittent generalized synchronization in unidirectionally coupled chaotic oscillators , 2005 .

[30]  Ingo Fischer,et al.  Synchronization in simple network motifs with negligible correlation and mutual information measures. , 2012, Physical review letters.

[31]  Jun Muramatsu,et al.  Common-chaotic-signal induced synchronization in semiconductor lasers. , 2007, Optics express.

[32]  Nearest neighbors, phase tubes, and generalized synchronization. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[33]  Ingo Fischer,et al.  High-Speed Optical Vector and Matrix Operations Using a Semiconductor Laser , 2013, IEEE Photonics Technology Letters.

[34]  Geert Morthier,et al.  Experimental demonstration of reservoir computing on a silicon photonics chip , 2014, Nature Communications.

[35]  Harald Haas,et al.  Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.

[36]  Edward Ott,et al.  Attractor reconstruction by machine learning. , 2018, Chaos.

[37]  K. Ikeda,et al.  Optical Turbulence: Chaotic Behavior of Transmitted Light from a Ring Cavity , 1980 .

[38]  Joni Dambre,et al.  Trainable hardware for dynamical computing using error backpropagation through physical media , 2014, Nature Communications.

[39]  B. Schrauwen,et al.  Reservoir computing and extreme learning machines for non-linear time-series data analysis , 2013, Neural Networks.

[40]  Miguel C. Soriano,et al.  A Unifying Analysis of Chaos Synchronization and Consistency in Delay-Coupled Semiconductor Lasers , 2019, IEEE Journal of Selected Topics in Quantum Electronics.

[41]  Thomas L. Carroll,et al.  Using reservoir computers to distinguish chaotic signals , 2018, Physical Review E.

[42]  Pyragas,et al.  Weak and strong synchronization of chaos. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[43]  Atsushi Uchida,et al.  Consistency of nonlinear system response to complex drive signals. , 2004 .

[44]  Ingo Fischer,et al.  Consistency properties of a chaotic semiconductor laser driven by optical feedback. , 2015, Physical review letters.

[45]  Alexey A Koronovskii,et al.  Inapplicability of an auxiliary-system approach to chaotic oscillators with mutual-type coupling and complex networks. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[46]  Jaideep Pathak,et al.  Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach. , 2018, Physical review letters.

[47]  Carroll,et al.  Synchronization in chaotic systems. , 1990, Physical review letters.

[48]  L. Tsimring,et al.  Generalized synchronization of chaos in directionally coupled chaotic systems. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[49]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[50]  Toshiyuki Yamane,et al.  Recent Advances in Physical Reservoir Computing: A Review , 2018, Neural Networks.

[51]  Kestutis Pyragas,et al.  Design of a negative group delay filter via reservoir computing approach: Real-time prediction of chaotic signals , 2019, Physics Letters A.

[52]  Robert A. Legenstein,et al.  2007 Special Issue: Edge of chaos and prediction of computational performance for neural circuit models , 2007 .

[53]  Laurent Larger,et al.  Optimal nonlinear information processing capacity in delay-based reservoir computers , 2014, Scientific Reports.

[54]  Atsushi Uchida,et al.  Local conditional Lyapunov exponent characterization of consistency of dynamical response of the driven Lorenz system. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[55]  Laurent Larger,et al.  Dynamical complexity and computation in recurrent neural networks beyond their fixed point , 2018, Scientific Reports.

[56]  Sudeshna Sinha,et al.  Introduction to focus issue: intrinsic and designed computation: information processing in dynamical systems--beyond the digital hegemony. , 2010, Chaos.

[57]  K. Ikeda Multiple-valued stationary state and its instability of the transmitted light by a ring cavity system , 1979 .

[58]  Kestutis Pyragas Conditional Lyapunov exponents from time series , 1997 .

[59]  Michael Small,et al.  Synchronization of chaotic systems and their machine-learning models. , 2019, Physical review. E.

[60]  M. C. Soriano,et al.  Consistency properties of chaotic systems driven by time-delayed feedback. , 2018, Physical review. E.

[61]  Michelle Girvan,et al.  Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model , 2018, Chaos.

[62]  J. Kurths,et al.  Three types of transitions to phase synchronization in coupled chaotic oscillators. , 2003, Physical review letters.

[63]  Daniel Brunner,et al.  Conditions for reservoir computing performance using semiconductor lasers with delayed optical feedback. , 2017, Optics express.

[64]  Jurgen Kurths,et al.  Synchronization in complex networks , 2008, 0805.2976.

[65]  Giulio Ruffini,et al.  Detection of Generalized Synchronization using Echo State Networks , 2017, Chaos.

[66]  H. Abarbanel,et al.  Generalized synchronization of chaos: The auxiliary system approach. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[67]  R. Brockett,et al.  Reservoir observers: Model-free inference of unmeasured variables in chaotic systems. , 2017, Chaos.