Effects of periodic forcing on the temporally correlated spikes of a semiconductor laser with feedback

Sorrentino, T.; Quintero-Quiroz, C.; Aragoneses, Andrés; Torrent, M. C.; Masoller, Cristina

doi:10.1364/oe.23.005571

Cited by 45 publications

(38 citation statements)

References 41 publications

(56 reference statements)

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“…We use a symbolic method of time-series analysis known as ordinal analysis 63, which has proven valuable for studying laser nonlinear dynamics47484952535462. With this method, each sequence of IEIs is transformed into a sequence of ordinal patterns (OPs), defined by considering the relative length of D consecutive IEIs.…”

Section: Resultsmentioning

confidence: 99%

Quantitative identification of dynamical transitions in a semiconductor laser with optical feedback

Quintero-Quiroz

Tiana-Alsina

Romà

et al. 2016

Sci Rep

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Identifying transitions to complex dynamical regimes is a fundamental open problem with many practical applications. Semi- conductor lasers with optical feedback are excellent testbeds for studying such transitions, as they can generate a rich variety of output signals. Here we apply three analysis tools to quantify various aspects of the dynamical transitions that occur as the laser pump current increases. These tools allow to quantitatively detect the onset of two different regimes, low-frequency fluctuations and coherence collapse, and can be used for identifying the operating conditions that result in specific dynamical properties of the laser output. These tools can also be valuable for analyzing regime transitions in other complex systems.

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Section: Resultsmentioning

confidence: 99%

Quantitative identification of dynamical transitions in a semiconductor laser with optical feedback

Quintero-Quiroz

Tiana-Alsina

Romà

et al. 2016

Sci Rep

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“…In our previous work [42] we used ordinal analysis to investigate how the correlations among several dropouts are affected by the modulation frequency and found that the minima and maxima of '210' OP probability were related to the noisy phase-locking of the spikes. Here we have focused on understanding how parameters that determine the natural spike rate (without modulation) affect this behavior.…”

Section: Discussionmentioning

confidence: 99%

“…Ordinal analysis [44] and other advanced nonlinear tools have been recently used to investigate the nonlinear dynamics of semiconductor lasers [24], [25], [42], [45]- [49]. Here we analyze the simulated and the experimental ISI sequences using ordinal analysis, as in [24], [25], [42].…”

Section: Ordinal Symbolic Analysismentioning

confidence: 99%

“…Here we analyze the simulated and the experimental ISI sequences using ordinal analysis, as in [24], [25], [42]. Each ISI sequence, {∆T i }, is transformed into a sequence of ordinal patterns (OPs), which are defined by considering the relative length of D consecutive ISIs and assigning them a symbol that indicates their relative length, in the same order as they appear in the sequence.…”

Section: Ordinal Symbolic Analysismentioning

confidence: 99%

“…We find that the differences in the mean ISI for the different modulation frequencies can be large or small, depending on the laser parameters. We then use ordinal analysis [24], [25], [42] to investigate the effect of varying the spike rate in the spike correlations. We find that higher spike rates wash out the effect of the current modulation in the temporal correlations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of the Spike Rate and Spike Correlations in Modulated Semiconductor Lasers With Optical Feedback

Sorrentino

Quintero-Quiroz

Torrent

et al. 2015

IEEE J. Select. Topics Quantum Electron.

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We investigate experimentally how changes in the external cavity length and DC current affect the mean inter-spikeinterval (ISI) in a modulated semiconductor laser with optical feedback operating in the low-frequency fluctuations regime. The variation of the mean ISI with the modulation frequency is shown to be more pronounced when time delay and DC current allow for low spike rate. We use the method of ordinal symbolic analysis to examine how time correlations (among 3, 4 and 5 consecutive laser spikes) change with the spike rate. This method is able to capture subtle changes, otherwise hidden in the dynamics. We find that higher spike rates wash out the effects of the modulation in the time correlations. Simulations using the Lang and Kobayashi model are in good qualitative agreement with the experimental observations.

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Principles of Neuromorphic Photonics

Shastri¹,

Tait²,

Lima³

et al. 2018

Unconventional Computing

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GLOSSARYBenchmark A standardized task that can be performed by disparate computing approaches, used to assess their relative processing merit in specific cases. Bifurcation A qualitative change in behavior of a dynamical system in response to parameter variation. Examples include cusp (from monostable to bistable), Hopf (from stable to oscillating), transcritical (exchange of stability between two steady states). Brain-inspired computing (a.k.a. neuro-inspired computing) A biologically inspired approach to build processors, devices, and computing models for applications including adaptive control, machine learning, and cognitive radio. Similarities with biological signal processing include architectural, such as distributed, representational, such as analog or spiking, or algorithmic, such as adaptation. Broadcast and Weight A multi-wavelength analog networking protocol in which multiple all photonic neuron outputs are multiplexed and distributed to all neuron inputs. Weights are reconfigured by tunable spectral filters. Excitability A far-from-equilibrium nonlinear dynamical mechanism underlying all-or-none responses to small perturbations. Fan-in The number of inputs to a neuron. Layered network A network topology consisting of a series of sets (i.e., layers) of neurons. The neurons in each set project their outputs only to neurons in the subsequent layer. Most commonly used type of network used for machine learning. Metric A quantity assessing performance of a device in reference to a specific computing approach. Microring weight bank A silicon photonic implementation of a reconfigurable spectral filter capable of independently setting transmission at multiple carrier wavelengths. Modulation The act of representing an abstract variable in a physical quantity, such as photon rate (i.e., optical power), free carrier density (i.e., optical gain), carrier drift (i.e., current). Electrooptic modulators are devices that convert from an electrical signal to the power envelope of an optical signal. Recently, the scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of neuromorphic photonics (Fig. 1). This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.tion processing, describe the photonic neural-network approaches being developed by our lab and others, and photonic integrated circuit (PIC) platforms, which have recently undergone rapid growth. PICs are becoming a

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Effects of periodic forcing on the temporally correlated spikes of a semiconductor laser with feedback

Cited by 45 publications

References 41 publications

Quantitative identification of dynamical transitions in a semiconductor laser with optical feedback

Quantitative identification of dynamical transitions in a semiconductor laser with optical feedback

Analysis of the Spike Rate and Spike Correlations in Modulated Semiconductor Lasers With Optical Feedback

Principles of Neuromorphic Photonics

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