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    This study introduces a novel associative-memory network using Kuramoto oscillators with enhanced coupling terms. The network demonstrates improved information coding, retrieval, and storage capacity, comparable to human subjects.

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    Area of Science:

    • Complex systems
    • Computational neuroscience
    • Network science

    Background:

    • The Kuramoto model is a fundamental framework for studying synchronization in coupled oscillator systems.
    • Associative memory networks are crucial for information storage and retrieval, mimicking biological memory functions.
    • Enhancing network coupling can potentially improve information processing capabilities.

    Purpose of the Study:

    • To introduce a new associative-memory network configuration based on Kuramoto oscillators.
    • To investigate the impact of modified coupling terms (second- and third-order Fourier expansion) on network performance.
    • To evaluate the network's capacity for information coding, retrieval, and storage, and compare it with human subject performance.

    Main Methods:

    • Developing a Kuramoto-type oscillator network with novel coupling terms.
    • Analyzing the network's response to various external stimuli.
    • Conducting comparative experiments involving pattern recognition tasks with both the network and human subjects.
    • Quantifying the network's storage capacity.

    Main Results:

    • The proposed network configuration exhibits enhanced capabilities for information coding and retrieval.
    • The network's performance in recognizing perturbed binary patterns is comparable to that of human subjects.
    • The modified coupling terms lead to an increased storage capacity within the network.

    Conclusions:

    • The novel Kuramoto-based associative-memory network offers a promising architecture for advanced information processing.
    • The findings suggest that tailored coupling mechanisms can significantly boost the efficiency of artificial memory systems.
    • This research bridges theoretical modeling with experimental validation, highlighting the potential of complex oscillator networks in artificial intelligence and neuroscience.