Excitability in a semiconductor laser with saturable absorber

Barbay, Sylvain; Kuszelewicz, R.; Yacomotti, A. M.

doi:10.1364/ol.36.004476

Cited by 153 publications

(111 citation statements)

References 21 publications

Supporting

Mentioning

111

Contrasting

Order By: Relevance

“…The spiking behavior of excitable photonic devices, which can be implemented by semiconductor technology, resembles the properties of biological neurons [39][40][41][42][43][44]. Networks of such excitable, nanophotonic devices would therefore correspond to a neural network implementation very close to their biological inspiration.…”

Section: Excitable Photonic Devices For Rcmentioning

confidence: 99%

“…When biased adequately close to a stability threshold, a laser with a saturable absorber becomes an excitable system [39,41,43,44]. In their dynamical behavior, these devices approximate the integrate and fire behavior of biological neurons with remarkable quality.…”

Section: Excitable Photonic Devices For Rcmentioning

confidence: 99%

“…In 2011, Barbay et al [39] demonstrated excitable neuron-like pulsing behavior of a monolithic semiconductor micro-pillar laser. The system is highly simplistic, only adding an additional quantum well in order to achieve excitability.…”

Section: Excitable Photonic Devices For Rcmentioning

confidence: 99%

See 2 more Smart Citations

Advances in photonic reservoir computing

2017

View full text Add to dashboard Cite

Abstract:We review a novel paradigm that has emerged in analogue neuromorphic optical computing. The goal is to implement a reservoir computer in optics, where information is encoded in the intensity and phase of the optical field. Reservoir computing is a bio-inspired approach especially suited for processing time-dependent information. The reservoir's complex and high-dimensional transient response to the input signal is capable of universal computation. The reservoir does not need to be trained, which makes it very well suited for optics. As such, much of the promise of photonic reservoirs lies in their minimal hardware requirements, a tremendous advantage over other hardware-intensive neural network models. We review the two main approaches to optical reservoir computing: networks implemented with multiple discrete optical nodes and the continuous system of a single nonlinear device coupled to delayed feedback.

show abstract

Section: Excitable Photonic Devices For Rcmentioning

confidence: 99%

Section: Excitable Photonic Devices For Rcmentioning

confidence: 99%

See 1 more Smart Citation

Advances in photonic reservoir computing

2017

View full text Add to dashboard Cite

show abstract

“…Neurons are well described by excitable systems, where type-I and type-II excitability can be distinguished [Hodgkin, 1948;Rinzel and Ermentrout, 1989;Izhikevich, 2000a;Lindner et al, 2004]. However, excitability is not limited to neural systems but was also observed in a wide range of natural and engineering systems including the cardiovascular systems [Yang and Wang, 2008], chemical systems like the BelousovZhabotinskii reaction [Mikhailov et al, 1994], optoelectronical oscillators [Rosin et al, 2011], Boolean networks realized by logic gates [Rosin et al, 2012], laser systems [Wün-sche et al, 2001;Goulding et al, 2007;Barbay et al, 2011], and glacial ocean oscillations [Ganopolski and Rahmstorf, 2002]. For a review on neural excitability see [Izhikevich, 2000a].…”

Section: Couplingmentioning

confidence: 99%

Controlling Synchronization Patterns in Complex Networks

Lehnert¹

2016

Springer Theses

View full text Add to dashboard Cite

In this thesis, I consider the control of synchronization in delay-coupled complex networks. As one main focus, several applications to neural networks will be discussed. In the first part, I focus on the stability of synchronization in complex networks meaning that the control is realized by considering the stability of synchrony in dependence on the parameters. In the second part, adaptive control of synchronization is studied. To this end, adaptive control algorithms are developed that tune the system parameters such that the desired control goal is reached.Besides zero-lag synchrony -a state where all nodes follow the same dynamics without a phase lag -groups and cluster states are considered, i.e., states where the network consists of several groups where the nodes within one group are in zero-lag synchrony and, in the case of cluster synchrony, with a constant phase lag between the clusters.The stability of synchronization can be accessed via the master stability function [Pecora and Carroll, 1998]. This convenient tool allows for treating the node dynamics and the network topology in two separate steps, and, thus, allows for a quite general treatment of different network topologies. In this thesis, I will discuss the generalization of the master stability function to group and cluster states and to non-smooth systems.The master stability function can be used to investigate synchrony in neural networks. Neurons are excitable systems where type-I and type-II excitability can be distinguished. Here, the stability of synchrony for both types in complex networks with excitatory and inhibitory links is investigated on two generic models, namely the normal form of the saddle-node infinite period bifurcation and the FitzHugh-Nagumo system. Furthermore, synchronization in systems with heterogeneous delays or node parameters is studied.In situations where parameters are unknown or drift, adaptive control methods are useful since they allow for an automatically realized adaption of the control parameters. A convenient adaptive method is the speed-gradient method that minimizes a predefined goal function [Fradkov, 1979[Fradkov, , 2007. I first apply this method in the control of an unstable focus and an unstable periodic orbit embedded in the Rössler attractor. Furthermore, I show that clusters states in networks of delayed coupled Stuart-Landau oscillators can be controlled by adaptively tuning the phase of the complex coupling strength or by adapting the topology. The first method is particularly simple because only one parameter has to be adapted, while the second method is more reliable in the sense that its success is widely independent of the choice of the control parameters and the initial conditions. ZUSAMMENFASSUNGIn dieser Arbeit wird die Kontrolle von Synchronisation in komplexen Netzwerken mit retardierten Kopplungen untersucht. Dabei werden verschiedene Anwendungen auf neuronale Netzwerke diskutiert. Der erste Teil der Arbeit beschäftigt sich mit der Stabilität von Synchronisation in komplexen Ne...

show abstract

“…Theoretically, they outperform the computational power of non-spiking artificial neural network types [1]. Given the natural appearance of excitability in many different non-linear optical components, both lasing [2,3,4,5,6,7] and non-lasing [8,9,10], there is an intrinsic advantage of implementing such networks in photonic hardware as this would allow to operate at time-scales that are orders of magnitude faster than typical biological and electronic implementations [11]. In this article, microdisk lasers are being proposed as a basic building block for an integrated photonic SNN platform.…”

Section: Introductionmentioning

confidence: 99%

Excitation transfer between optically injected microdisk lasers

Vaerenbergh¹,

Alexander²,

Dambre³

et al. 2013

Opt. Express

View full text Add to dashboard Cite

Abstract:Recently, we have theoretically demonstrated that optically injected microdisk lasers can be tuned in a class I excitable regime, where they are sensitive to both inhibitory and excitatory external input pulses. In this paper, we propose, using simulations, a topology that allows the disks to react on excitations from other disks. Phase tuning of the intermediate connections allows to control the disk response. Additionally, we investigate the sensitivity of the disk circuit to deviations in driving current and locking signal wavelength detuning. Using state-of-the-art fabrication techniques for microdisk laser, the standard deviation of the lasing wavelength is still about one order of magnitude too large. Therefore, compensation techniques, such as wavelength tuning by heating, are necessary.

show abstract

Excitability in a semiconductor laser with saturable absorber

Cited by 153 publications

References 21 publications

Advances in photonic reservoir computing

Advances in photonic reservoir computing

Controlling Synchronization Patterns in Complex Networks

Excitation transfer between optically injected microdisk lasers

Contact Info

Product

Resources

About