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Article Dans Une Revue Chaos: An Interdisciplinary Journal of Nonlinear Science Année : 2023

Resonant properties of the memory capacity of a laser-based reservoir computer with filtered optoelectronic feedback

Résumé

We provide a comprehensive analysis of the resonant properties of the memory capacity of a reservoir computer based on a semiconductor laser subjected to time-delayed filtered optoelectronic feedback. Our analysis reveals first how the memory capacity decreases sharply when the input-data clock cycle is slightly time-shifted from the time delay or its multiples. We attribute this effect to the inertial properties of the laser. We also report on the damping of the memory-capacity drop at resonance with a decrease of the virtual-node density and its broadening with the filtering properties of the optoelectronic feedback. These results are interpretated using the eigenspectrum of the reservoir obtained from a linear stability analysis. Then, we unveil an invariance in the minimum value of the memory capacity at resonance with respect to a variation of the number of nodes if the number is big enough and quantify how the filtering properties impact the system memory in and out of resonance.
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Dates et versions

hal-04675611 , version 1 (22-08-2024)

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Gleb Danilenko, Anton Kovalev, Evgeny Viktorov, Alexandre Locquet, David S. Citrin, et al.. Resonant properties of the memory capacity of a laser-based reservoir computer with filtered optoelectronic feedback. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2023, 33 (11), pp.113125. ⟨10.1063/5.0172039⟩. ⟨hal-04675611⟩
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