Laser dynamical reservoir computing with consistency: an approach of a chaos mask signal

Nakayama, J.; Kanno, Kazutaka; Uchida, Atsushi

doi:10.1364/oe.24.008679

Cited by 178 publications

(102 citation statements)

References 31 publications

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“…Fast chaotic laser outputs have been used for applications in fast physical random number generation [16][17][18], secure key distribution [19,20], and reservoir computing [21,22]. Figure 1 shows the decision-making scheme based on the TOW method using chaotic temporal waveforms of the semiconductor laser.…”

Section: Tug-of-war Methods Using a Chaotic Semiconductor Lasermentioning

confidence: 99%

Memory Effect on Adaptive Decision Making with a Chaotic Semiconductor Laser

et al. 2018

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We investigate the effect of a memory parameter on the performance of adaptive decision making using a tug-of-war method with the chaotic oscillatory dynamics of a semiconductor laser. We experimentally generate chaotic temporal waveforms of the semiconductor laser with optical feedback and apply them for adaptive decision making in solving a multiarmed bandit problem that aims at maximizing the total reward from slot machines whose hit probabilities are dynamically switched. We examine the dependence of making correct decisions on different values of the memory parameter. The degree of adaptivity is found to be enhanced with a smaller memory parameter, whereas the degree of convergence to the correct decision is higher for a larger memory parameter. The relations among the adaptivity, environmental changes, and the difficulties of the problem are also discussed considering the requirement of past decisions. This examination of ultrafast adaptive decision making highlights the importance of memorizing past events and paves the way for future photonic intelligence.

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Section: Tug-of-war Methods Using a Chaotic Semiconductor Lasermentioning

confidence: 99%

Memory Effect on Adaptive Decision Making with a Chaotic Semiconductor Laser

et al. 2018

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“…A similar system was also employed to demonstrate highspeed optical vector and matrix operations [69]. Numerical simulations of this kind of system suggest that the information processing capabilities can still be improved by either increasing the injection strength [70] or by changing the input mask [71].…”

Section: All Optical Delay-based Reservoir Computingmentioning

confidence: 99%

Advances in photonic reservoir computing

2017

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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.

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“…In those implementations, the nonlinear response of the reservoir is provided by passive nonlinearity such as saturable absorption of a semiconductor mirror [9][10][11] or by active devices such as optoelectronic modulators [5,8], optical amplifiers [3], or semiconductor lasers [7]. These experiments have been supported by numerical simulations [8,[12][13][14][15]. Numerical simulations also have shown that the different modes of multimode lasers subject to optical delayed feedback can be used independently to process independent tasks in parallel [16].…”

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“…(ii) In the usual procedure of RC, the injection times of the input data (i.e., the inverse of the processing speeds) are close or correspond to the time delay. In addition, in previous experiments, long time delays (time delays much larger than the system's characteristic time) have been used [4][5][6][7][8][9][10][11][12][13][14]16]. We explore here whether time delays less than or comparable to the laser relaxation time can also be suitable for RC systems.…”

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“…The optimal values of these weights are those for which the summation of all the different node responses always approaches the associated target as closely as possible. They can typically be determined with an offline training procedure using digital computers [5][6][7][8][11][12][13][14] or an online training procedure using a field-programmable gate array [17]. In our case, the training is done offline.…”

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Prediction performance of reservoir computing systems based on a diode-pumped erbium-doped microchip laser subject to optical feedback

et al. 2017

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Reservoir computing (RC) systems are computational tools for information processing that can be fully implemented in optics. Here, we experimentally and numerically show that an optically pumped laser subject to optical delayed feedback can yield similar results to those obtained for electrically pumped lasers. Unlike with previous implementations, the input data are injected at a time interval that is much larger than the time-delay feedback. These data are directly coupled to the feedback light beam. Our results illustrate possible new avenues for RC implementations for prediction tasks. Reservoir computing (RC) is a brain-inspired concept for information processing that has been recently demonstrated to be efficient for solving practical time-dependent tasks [1,2]. RC systems operate by ensuring nonlinear mapping between the input and the output, thereby allowing a variety of information processing through training. To perform well, an RC system typically requires high dimensionality and nonlinearity. Traditionally, high dimensionality is obtained by randomly interconnecting a large number of neurons, while the needed nonlinearity can be implemented through sigmoidal activation functions. For example, with an ensemble of 16 interconnected semiconductor optical amplifiers, state-of-the-art performance has been achieved [3].Alternatively, state-of-the-art performance also has been obtained by relying on a single dynamical nonlinear node subject to delayed feedback [4]. This configuration (typically referred to as the delay-based RC) has the advantage of being easy to train and to implement experimentally. It has led to several implementations, even at high processing speeds, using stand-alone commercial telecommunication components [5][6][7][8]. The main differences between these experiments are the type of nonlinearity used and how the input matches with the period of the delay line. In those implementations, the nonlinear response of the reservoir is provided by passive nonlinearity such as saturable absorption of a semiconductor mirror [9][10][11] or by active devices such as optoelectronic modulators [5,8], optical amplifiers [3], or semiconductor lasers [7]. These experiments have been supported by numerical simulations [8,[12][13][14][15]. Numerical simulations also have shown that the different modes of multimode lasers subject to optical delayed feedback can be used independently to process independent tasks in parallel [16]. In all cases, the readout layer is trained (using some form of regression) from the state vectors of the reservoir in response to the training data.Until now, only one experiment has been dedicated to RC systems, in which the processing was done from the response provided by a laser subject to optical delayed feedback [7]. The laser used in this experiment was an electrically pumped singlelongitudinal-mode laser, and the input data were either electrically injected by modulating the pump current or optically injected into the reservoir through optical injection using another laser....

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Laser dynamical reservoir computing with consistency: an approach of a chaos mask signal

Cited by 178 publications

References 31 publications

Memory Effect on Adaptive Decision Making with a Chaotic Semiconductor Laser

Memory Effect on Adaptive Decision Making with a Chaotic Semiconductor Laser

Advances in photonic reservoir computing

Prediction performance of reservoir computing systems based on a diode-pumped erbium-doped microchip laser subject to optical feedback

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