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Memristive Bionic Memory Circuit Implementation and Its Application in Multisensory Mutual Associative Learning

Mingxuan Jiang, Zhigang Zeng

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    This study introduces a novel memristive bionic memory circuit that mimics human memory functions. The circuit efficiently processes sensory information, demonstrating potential for advanced brain-inspired artificial intelligence systems.

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    Area of Science:

    • Neuroscience and Artificial Intelligence
    • Materials Science and Electronic Engineering

    Background:

    • Memory is crucial for cognition in both biological organisms and artificial intelligence.
    • Existing artificial systems often lack the complex memory functions observed in biological brains.
    • Memristors offer unique in-memory computing capabilities for advanced hardware.

    Purpose of the Study:

    • To propose a memristive bionic memory circuit inspired by the human memory model.
    • To implement essential memory functions including learning, forgetting, and recall.
    • To construct a multisensory associative learning network for integrated sensory processing.

    Main Methods:

    • Designed a circuit comprising receptor, sensory neuron, short-term memory (STM), and long-term memory (LTM) modules.
    • Utilized memristors' in-memory computing for diverse memory operations.
    • Developed a multisensory mutual associative learning network using bionic memory units.

    Main Results:

    • Successfully simulated various memory functions: sensation, learning, forgetting, recall, consolidation, reconsolidation, retrieval, and reset.
    • Demonstrated bidirectional association, enhancement, and extinction of multisensory information.
    • Achieved multisensory integration, mimicking the synthetic process of information from different sensory channels.
    • Verified high robustness, low area overhead, and low power consumption via PSPICE simulations.

    Conclusions:

    • The proposed memristive bionic memory circuit effectively replicates human memory functions.
    • The multisensory associative learning network shows promise for integrated sensory processing.
    • This work offers a viable pathway for future research in brain-inspired associative learning networks and artificial intelligence.