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    We analyzed how system response time impacts delay-based reservoir computers. Increasing the delay time beyond data injection time significantly reduces computational capacity loss, improving performance on benchmark tasks.

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    Area of Science:

    • Computational neuroscience
    • Machine learning
    • Complex systems

    Background:

    • Reservoir computing is a powerful machine learning paradigm.
    • Delay-based reservoir computers offer unique advantages but face computational capacity degradation.
    • System response time is a critical factor influencing performance.

    Purpose of the Study:

    • To investigate the impact of system response time on computational capacity in delay-based reservoir computers.
    • To identify strategies for mitigating performance degradation.
    • To demonstrate performance improvements through parameter optimization.

    Main Methods:

    • Analysis of computational capacity degradation.
    • Systematic variation of delay time and data injection time.
    • Benchmarking on diverse computational tasks.

    Main Results:

    • Computational capacity degradation is directly linked to system response time.
    • A significant reduction in degradation occurs when delay time exceeds data injection time.
    • Optimized parameter settings led to demonstrable performance improvements.

    Conclusions:

    • System response time is a key parameter for optimizing delay-based reservoir computers.
    • Tuning the delay time relative to data injection time is crucial for maintaining computational capacity.
    • This study provides a pathway for enhanced performance in complex computational tasks.