Predicting bursting in a complete graph of mixed population through reservoir computing

This article presents a prediction of spiking and bursting dynamics in globally coupled networks, using reservoir computing-based learning procedure.

[1]  Ranjit Kumar Upadhyay,et al.  Emergence of bursting in a network of memory dependent excitable and spiking leech-heart neurons , 2020, Journal of the Royal Society Interface.

[2]  S. K. Dana,et al.  Dragon-king-like extreme events in coupled bursting neurons. , 2018, Physical review. E.

[3]  S. K. Dana,et al.  Spiking and Bursting in Josephson Junction , 2006, IEEE Transactions on Circuits and Systems II: Express Briefs.

[4]  Patrick Crotty,et al.  Josephson junction simulation of neurons. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Peter Ford Dominey,et al.  Real-Time Parallel Processing of Grammatical Structure in the Fronto-Striatal System: A Recurrent Network Simulation Study Using Reservoir Computing , 2013, PloS one.

[6]  Vladimir A. Maksimenko,et al.  Feed-forward artificial neural network provides data-driven inference of functional connectivity. , 2019, Chaos.

[7]  Steven L. Brunton,et al.  Deep learning for universal linear embeddings of nonlinear dynamics , 2017, Nature Communications.

[8]  Christoph Räth,et al.  Good and bad predictions: Assessing and improving the replication of chaotic attractors by means of reservoir computing. , 2019, Chaos.

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

[10]  Frank C. Hoppensteadt,et al.  Dynamics of the Josephson junction , 1978 .

[11]  P. K. Roy,et al.  Multicluster oscillation death and chimeralike states in globally coupled Josephson Junctions. , 2017, Chaos.

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

[14]  Chittaranjan Hens,et al.  Perfect synchronization in networks of phase-frustrated oscillators , 2017 .

[15]  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.

[16]  Eugene M. Izhikevich,et al.  Neural excitability, Spiking and bursting , 2000, Int. J. Bifurc. Chaos.

[17]  Louis M. Pecora,et al.  Network Structure Effects in Reservoir Computers , 2019, Chaos.

[18]  Francesco Sorrentino,et al.  Stability Analysis of Reservoir Computers Dynamics via Lyapunov Functions , 2019, Chaos.

[19]  J. Rinzel,et al.  Rhythmogenic effects of weak electrotonic coupling in neuronal models. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

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

[21]  S. K. Dana,et al.  Bursting dynamics in a population of oscillatory and excitable Josephson junctions. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  B. Schrauwen,et al.  Isolated word recognition with the Liquid State Machine: a case study , 2005, Inf. Process. Lett..

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

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

[25]  Tomasz Kapitaniak,et al.  Network-induced multistability through lossy coupling and exotic solitary states , 2020, Nature Communications.

[26]  Michael Small,et al.  The reservoir's perspective on generalized synchronization. , 2019, Chaos.

[27]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[28]  Chris G. Antonopoulos,et al.  Emergence of Mixed Mode Oscillations in Random Networks of Diverse Excitable Neurons: The Role of Neighbors and Electrical Coupling , 2020, Frontiers in Computational Neuroscience.

[29]  Syamal K. Dana,et al.  Chaotic dynamics in Josephson junction , 2001 .

[30]  Zehong Yang,et al.  Short-term stock price prediction based on echo state networks , 2009, Expert Syst. Appl..

[31]  G. Ermentrout,et al.  Parabolic bursting in an excitable system coupled with a slow oscillation , 1986 .

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

[33]  Dianhui Wang,et al.  A decentralized training algorithm for Echo State Networks in distributed big data applications , 2016, Neural Networks.

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

[35]  Oleg V Maslennikov,et al.  Collective dynamics of rate neurons for supervised learning in a reservoir computing system. , 2019, Chaos.

[36]  Jordi Garcia-Ojalvo,et al.  Characterization of the non-stationary nature of steady-state visual evoked potentials using echo state networks , 2019, PloS one.

[37]  Arindam Mishra,et al.  Coherent libration to coherent rotational dynamics via chimeralike states and clustering in a Josephson junction array. , 2017, Physical review. E.

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

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

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

[41]  Y. Kuramoto,et al.  A Soluble Active Rotater Model Showing Phase Transitions via Mutual Entertainment , 1986 .

[42]  SchmidhuberJürgen Deep learning in neural networks , 2015 .

[43]  Chittaranjan Hens,et al.  Transition to synchrony in degree-frequency correlated Sakaguchi-Kuramoto model. , 2017, Physical review. E.

[44]  Luís F. Seoane,et al.  Evolutionary aspects of reservoir computing , 2018, Philosophical Transactions of the Royal Society B.

[45]  J. Hindmarsh,et al.  A model of neuronal bursting using three coupled first order differential equations , 1984, Proceedings of the Royal Society of London. Series B. Biological Sciences.

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

[47]  Stephen Lynch,et al.  Oscillatory Threshold Logic , 2012, PloS one.