Abstract
Synchronization among coupled elements is universally observed in nonlinear systems, such as in food chain and coupled synaptic neurons as well as coupled semiconductor lasers. Using nonlinear elements showing similar characteristics of synaptic neurons, the behaviors of real neural networks can be effectively investigated and information processing that is similar to the human brain can be performed based on such systems. The typical features of neurons are excitability of the output from external stimuli, inhibition of conflicted inputs, spiking oscillations even including chaos, and synchronization among coupled neurons. As nonlinear dynamics point of view, semiconductor lasers have the similarity with synaptic neurons. Also, neuro-inspired information processing, which mimics the functions of the neuron dynamics, is discussed using nonlinear delay feedback systems such as a semiconductor lasers with optical feedback. The keys for common dynamics of such systems are the consistency of drive-response nonlinear systems and the synchronization properties between distant nonlinear elements. In this chapter, starting from a small number of coupled semiconductor lasers, we investigate the dynamics and synchronization properties of many coupled semiconductor laser networks. We also present a new type of information process and its application based on reservoir computing, in which a single semiconductor laser subjected to optical feedback is used as a reservoir in the neural networks.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
L. Appeltant, M.C. Soriano, V. Van der Sande, J. Danckaert, S. Massa, J. Dambre, B. Schrauwen, C.R. Mirasso, I. Fischer, Information processing using a single dynamical node as complex system. Nat. Commun. 2, 468–474 (2011)
L. Appeltant, Reservoir computing based on delay-dynamical systems. Joint Ph.D. Thesis, Vrije Universiteit Brussel and Universitat de les Illes Balears, (2012)
L. Appeltant, G. Van der Sande, J. Danckaert, I. Fischer, Constructing optimized binary masks for reservoir computing with delay systems. Sci. Rep. 4, 3629–3633 (2014)
A. Argyris, M. Bourmpos, D. Syvridis, Experimental synchrony of semiconductor lasers in coupled networks. Opt. Express 24, 5600–5615 (2016)
Y. Aviad, I. Reidler, M. Zigzag, M. Rosenbluh, I. Kanter, Synchronization in small networks of time-delay coupled chaotic diode lasers. Opt. Express 20, 4352–4359 (2012)
M. Beierlein, J.R. Gibson, B.W. Connors, A network of electrically coupled interneurons drives synchronized inhibition in neocortex. Nat. Neurosci. 3, 904–910 (2000)
M. Bourmpos, A. Argyris, D. Syvridis, Sensitivity analysis of a star optical network based on mutually coupled semiconductor lasers. J. Lightwave Technol. 30, 2618–2624 (2012)
M. Bourmpos, A. Argyris, D. Syvridis, Analysis of the bubbling effect in synchronized networks with semiconductor lasers. IEEE Photonic Technol. Lett. 25, 817–820 (2013)
D. Brunner, M.C. Soriano, C.R. Mirasso, I. Fischer, Parallel photonic information processing at gigabyte per second data rates using transient states. Nat. Commun. 4, 1364–1400 (2013)
J.M. Buldú, M.C. Torrent, J. García-Ojalvo, Synchronization in semiconductor laser rings. J. Lightwave Technol. 25, 1549–1554 (2007)
E. Cohen, E. Rosenbluh, I. Kanter, Phase transition in crowd synchrony of delay-coupled multilayer laser networks. Opt. Express 18, 19683–19689 (2012)
F. Duport, B. Schneider, A. Smerieri, M. Haelterman, S. Massar, All-optical reservoir computing. Opt. Express 20, 22783–22795 (2012)
B. Eckhardt, E. Ott, S.H. Strogatz, D.M. Abrams, A. McRobie, Modeling walker synchronization on the Millennium Bridge. Phys. Rev. E 75, 021110-1–021110-10 (2007)
M. Escalona-Moran, M.C. Soriano, I. Fischer, C.R. Mirasso, Electrocardiogram classification using reservoir computing with logistic regression. IEEE J. Biomed. Health Inf. 19, 892–898 (2015)
I. Fischer, R. Vicente, J.M. Buldú, M. Peil, C.R. Mirasso, M.C. Torrent, J. Garciá-Ojalvo, Zero-lag long-range synchronization via dynamical relaying. Phys. Rev. Lett. 97, 123902-1–123902-4 (2006)
T. Fukuda, T. Kosaka, W. Singer, R.A.W. Galuske, Gap junctions among dendrites of cortical GABAergic neurons establish a dense and widespread intercolumnar network. J. Neurosci. 26, 3434–3443 (2006)
C.M. González, C. Masoller, M.C. Torrent, J. García-Ojalvo, Synchronization via clustering in a small delay-coupled laser network. Europhys. Lett. 79, 64003-1–64003-6 (2007)
R.W. Guillery, Review: observations of synaptic structures: origins of the neuron doctrine and its current status. Phil. Trans. Ray. Soc. B 360, 1281–1307 (2005)
D. Hansel, G. Mato, C. Meunier, Phase dynamics for weakly coupled Hodgkin-Huxley neurons. Europhys. Lett. 23, 367–372 (1993)
T. Heil, I. Fischer, W. Elsäßer, J. Mulet, C.R. Mirasso, Chaos synchronization and spontaneous symmetry-breaking in symmetrically delay-coupled semiconductor lasers. Phys. Rev. Lett. 86, 795–798 (2001)
S. Heiligenthal, T. Dahms, S. Yanchuk, T. Jüngling, V. Flunkert, I. Kanter, E. Schöll, W. Kinzel, Strong and weak chaos in nonlinear networks with time-delayed couplings. Phys. Rev. Lett. 107, 234102-1–234102-5 (2011)
S. Heiligenthal, T. Jüngling, O. D’Huys, D.A. Arroyo-Almanza, M.C. Soriano, I. Fischer, I. Kanter, W. Kinzel, Strong and weak chaos in networks of semiconductor lasers with time-delayed couplings. Phys. Rev. E 88, 012902-1–012902-13 (2013)
H. Hicke, M.A. Escalona-Morán, D. Brunner, M.C. Soriano, I. Fischer, C.R. Mirasso, Information processing using transient dynamics of semiconductor lasers subject to delayed feedback. IEEE J. Sel. Top. Quantum Electron. 19, 1501610-1–1501610-10 (2013)
A.L. Hodgkin, A.F. Huxley, A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117, 500–544 (1952)
J. Hopfield, Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. 79, 2554–2558 (1982)
J. Hopfield, Neurons with graded response have collective computational properties like those of two-state neurons. Proc. Natl. Acad. Sci. 81, 3088–3092 (1984)
U. Huebner, N.B. Abraham, C.O. Weiss, Dimensions and entropies of chaotic intensity pulsations in a single-mode far-infrared NH3 laser. Phys. Rev. A 40, 6354–6365 (1989)
C. Huygens, Christiaan Huygens’ the Pendulum Clock, or, Geometrical Demonstrations Concerning the Motion of Pendula as Applied to Clocks (Iowa State Press, 1986)
H. Jaeger, The ‘echo state’ approach to analyzing and training recurrent neural networks. Technical Report GMD Report 148, German National Research Center for Information Technology (2001)
H. Jaeger, H. Haas, Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304, 78–80 (2004)
I. Kanter, M. Zigzag, A. Englert, F. Geissler, W. Kinzel, Synchronization of unidirectional time delay chaotic networks and the greatest common divisor. Europhys. Lett. 93, 60003-1–60003-6 (2011a)
I. Kanter, E. Kopelowitz, R. Vardi, M. Zigzag, W. Kinzel, M. Abeles, D. Cohen, Nonlocal mechanism for cluster synchronization in neural circuits. Europhys. Lett. 93, 66001-1–66001-6 (2011b)
I. Kanter, E. Kopelowitz, R. Vardi, M. Zigzag, D. Cohen, W. Kinzel, Nonlocal mechanism for synchronization of time delay networks. J. Stat. Phys. 145, 713–733 (2011c)
L. Larger, M.C. Soriano, D. Brunner, L. Appeltant, J.M. Gutierrez, L. Pesquera, C.R. Mirasso, I. Fischer, Photonic information processing beyond turing: an optoelectronic implementation of reservoir computing. Opt. Express 5, 188–200 (2012)
W. Maass, T. Natschläger, H. Markram, Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14, 2531–2560 (2002)
R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, U. Alon, Network motifs: simple building blocks of complex networks. Science 298, 824–828 (2002)
J. Mulet, C.R. Mirasso, T. Heil, I. Fischer, Synchronization scenario of two distant mutually coupled semiconductor lasers. J. Opt. B: Quantum Semiclass. Opt. 6, 97–105 (2004)
J. Nakayama, K. Kanno, A. Uchida, Laser dynamical reservoir computing with consistency: an approach of a chaos mask signal. Opt. Express 24, 8679–8692 (2016)
M. Nixon, M. Friedman, E. Ronen, A.A. Friesem, N. Davidson, I. Kanter, Synchronized cluster formation in coupled laser networks. Phys. Rev. Lett. 106, 223901-1–223901-4 (2011)
M. Nixon, M. Friedman, E. Ronen, A.A. Friesem, N. Davidson, I. Kanter, Controlling synchronization in large laser networks. Phys. Rev. Lett. 108, 214101-1–214101-5 (2012)
J. Ohtsubo, R. Ozawa, M. Nanbu, Synchronization of small nonlinear networks in chaotic semiconductor lasers. Jpn. J. Appl. Phys. 54, 072702-1–072702-6 (2015)
Y. Paquot, F. Duport, F. Smerieri, J. Dambre, B. Schrauwen, M. Haelterman, S. Massar, Optoelectronic reservoir computing. Sci. Rep. 2, 287–292 (2012)
T. Pérez, A. Uchida, Reliability and synchronization in a delay-coupled neuronal network with synaptic plasticity. Phys. Rev. E 83, 061915-1–061915-6 (2011)
P.R. Prucnal, B.J. Shastri, T.F. de Lima, M.A. Nahmias, A.N. Tait, Recent progress in semiconductor excitable lasers for photonic spike processing. Adv. Opt. Photon. 8, 228–299 (2016)
D.P. Rosin, D. Rontani, D.J. Gauthier, E. Schöll, Control of synchronization patterns in neural-like Boolean networks. Phys. Rev. Lett. 110, 104102-1–104102-5 (2013)
M.C. Soriano, J. Garcıá-Ojalvo, C.R. Mirasso, I. Fischer, Complex photonics: dynamics and applications of delay-coupled semiconductors lasers. Rev. Mod. Phys. 85, 421–470 (2013a)
M.C. Soriano, S. Ortín, D. Brunner, L. Larger, C.R. Mirasso, I. Fischer, L. Pesquera, Optoelectronic reservoir computing: tackling noise-induced performance degradation. Opt. Express 21, 12–20 (2013b)
S.H. Strogatz, I. Stewart, Coupled oscillators and biological synchronization. Sci. Am. 269, 102–109 (1993)
S.H. Strogatz, D.M. Abrams, A. McRobie, B. Eckhardt, E. Ott, Crowd synchrony on the Millennium Bridge. Nature 438, 43–44 (2005)
R.D. Traub, M.A. Whittington, I.M. Stanford, J.G. Jefferys, A mechanism for generation of long-range synchronous fast oscillations in the cortex. Nature 383, 621–624 (1996)
A. Uchida, A. McAllister, R. Roy, Consistency of nonlinear system response to complex drive signals. Phys. Rev. Lett. 93, 244102-1–244102-4 (2004)
G. Van der Sande, M.C. Soriano, I. Fischer, C.R. Mirasso, Dynamics, correlation scaling, and synchronization behavior in rings of delay-coupled oscillators. Phys. Rev. E 77, 055202(R)-1–055202(R)-4 (2008)
R. Vardi, A. Wallach, E. Kopelowitz, M. Abeles, S. Marom, I. Kanter, Synthetic reverberating activity patterns embedded in networks of cortical neurons. Europhys. Lett. 97, 66002-1–66002-6 (2012)
R. Vicente, L.L. Gollo, C.R. Mirasso, I. Fischer, G. Pipa, Dynamical relaying can yield zero time lag neuronal synchrony despite long conduction delays. Proc. Natl. Acad. Sci. 105, 17157–17162 (2008)
A.S. Weigend, N.A. Gershenfeld, Time series prediction: forecasting the future and understanding the past. http://www-psych.stanford.edu/~andreas/Time-Series/SantaFe.html, 1993
S.Y. Xiang, A.J. Wen, W. Pan, Synchronization regime of star-type laser network with heterogeneous coupling delays. IEEE Photonic Technol. Lett. 28, 1988–1991 (2016)
J. Zamora-Munt, C. Masoller, J. García-Ojalvo, R. Roy, Crowd synchrony and quorum sensing in delay-coupled lasers. Phys. Rev. Lett. 105, 264101-1–264101-4 (2010)
W.L. Zhang, W. Pan, B. Luo, X.H. Zou, M.Y. Wang, One-to-many and many-to-one optical chaos communications using semiconductor lasers. IEEE Photonic Technol. Lett. 20, 712–714 (2008)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Ohtsubo, J. (2017). Semiconductor Laser Networks: Synchrony, Consistency, and Analogy of Synaptic Neurons. In: Semiconductor Lasers. Springer Series in Optical Sciences, vol 111. Springer, Cham. https://doi.org/10.1007/978-3-319-56138-7_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-56138-7_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-56137-0
Online ISBN: 978-3-319-56138-7
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)