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    Fully optimizing delay-coupled optoelectronic systems as machine-learning models significantly improves performance on phoneme recognition tasks compared to traditional reservoir computing methods.

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    Area of Science:

    • Optoelectronics
    • Machine Learning
    • Signal Processing

    Background:

    • Delay-coupled optoelectronic systems are recognized for their information processing potential.
    • Previous research utilized these systems within reservoir computing (RC) frameworks, often treating them as random dynamical systems.

    Purpose of the Study:

    • To investigate delay-coupled optoelectronic systems as fully optimizable machine-learning models.
    • To apply advanced optimization techniques to enhance their information processing capabilities.

    Main Methods:

    • Utilized an extension of backpropagation through time (BPTT), an algorithm for recurrent neural networks.
    • Trained the optoelectronic system parameters for a challenging phoneme recognition task.

    Main Results:

    • Full optimization of system parameters led to substantial performance gains.
    • The optimized system outperformed the conventional RC approach for the phoneme recognition task.

    Conclusions:

    • Delay-coupled optoelectronic systems can be effectively treated and optimized as true machine-learning models.
    • This optimization approach offers significant advantages over standard reservoir computing for complex tasks like phoneme recognition.