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Memristive Circuit Implementation of Operant Cascaded With Classical Conditioning.

Chao Yang, Xiaoping Wang, Zhanfei Chen

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    This study introduces a novel memristive circuit for operant conditioning (OC) and classical conditioning (CC) associative memory. The proposed circuit mimics brain learning, offering enhanced adaptability and efficient in-memory computing.

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    Area of Science:

    • Neuroscience and Neuromorphic Engineering
    • Artificial Intelligence and Machine Learning

    Background:

    • Classical conditioning (CC) and operant conditioning (OC) are fundamental biological learning mechanisms.
    • Existing memristive circuits primarily focus on CC, with limited imitation of OC and OC-CC cascaded systems.

    Purpose of the Study:

    • To propose and simulate a novel OC-CC cascaded memristive circuit.
    • To achieve bio-like learning functions and enhanced environmental adaptability.
    • To demonstrate on-line, in-situ learning and in-memory computing capabilities.

    Main Methods:

    • Design of an operant conditioning (OC) memristive circuit with bio-like functions.
    • Cascading the OC circuit with a classical conditioning (CC) memristive circuit.
    • Simulation of the proposed OC-CC cascaded circuit using PSPICE.

    Main Results:

    • The OC circuit successfully demonstrated random exploration, feedback learning, and decision-making.
    • The cascaded OC-CC circuit exhibited richer associative memories and improved environmental adaptability.
    • Simulations confirmed on-line, in-situ learning and in-memory computing, outperforming von Neumann architecture in power and hardware overhead.

    Conclusions:

    • The proposed OC-CC cascaded memristive circuit effectively mimics complex biological learning processes.
    • This design offers a brain-like processing approach with significant advantages in efficiency.
    • Provides a viable pathway for developing large-scale bionic learning systems.