Semiconductor Laser Networks: Synchrony, Consistency, and Analogy of Synaptic Neurons
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.
KeywordsSemiconductor Laser Optical Feedback Chaos Synchronization Nonlinear Element Couple Laser
- L. Appeltant, Reservoir computing based on delay-dynamical systems. Joint Ph.D. Thesis, Vrije Universiteit Brussel and Universitat de les Illes Balears, (2012)Google Scholar
- C. Huygens, Christiaan Huygens’ the Pendulum Clock, or, Geometrical Demonstrations Concerning the Motion of Pendula as Applied to Clocks (Iowa State Press, 1986)Google Scholar
- 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)Google Scholar
- 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)Google Scholar
- 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