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    This study introduces a novel neuromorphic circuit for automatic learning using operant conditioning. The circuit incorporates time memory and addictiveness, enabling adaptation to complex scenarios and referencing biological behavior for advanced brain-inspired systems.

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    Area of Science:

    • Neuroscience
    • Computational Neuroscience
    • Artificial Intelligence

    Background:

    • Traditional operant conditioning circuits often oversimplify feedback mechanisms.
    • Limited research addresses complex feedback outcomes and temporal uncertainty in learning circuits.

    Purpose of the Study:

    • To propose a novel neuromorphic circuit for automatic learning based on operant conditioning.
    • To incorporate addictiveness and time memory for enhanced learning capabilities.
    • To enable adaptation to complex environments and continuous memory reshaping.

    Main Methods:

    • Development of a neuromorphic circuit comprising hunger output, neuron, excitement output, memristor-based decision, and memory/feedback modules.
    • Implementation of stochastic feedback processes to achieve output excitement and addiction.
    • Integration of hunger and satiety mechanisms to model biological behavior and exploration desire.

    Main Results:

    • The circuit successfully forms memories of intervals between rewards.
    • It demonstrates adaptability to complex scenarios through its integrated functions.
    • The system achieves automatic learning by applying operant conditioning principles.

    Conclusions:

    • The proposed neuromorphic circuit effectively models operant conditioning with addictiveness and time memory.
    • This approach provides a foundation for more intelligent brain-inspired neural systems.
    • The study highlights the potential of incorporating biological behavior into artificial learning systems.