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A brain-inspired neurodynamic model for efficient sequence memory
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DOI：10.1016/j.neucom.2025.131849.png)
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Abstract
En 中文
Sequence memory, such as memorizing digit strings and music, is a fundamental cognitive function involving multiple brain regions. It allows for both goal-based retrieval and context-based retrieval after memory formation, which is essential for memory integration and adaptability in improved multitasking performance. However, existing brain-inspired neurodynamic models face significant challenges, including the implementation of bidirectional retrieval functions and the allocation of neurons according to task-specific requirements. Inspired by physiological clues, we hypothesize that sequence memory should involve an information processing architecture corresponding to perception, memory, and semantic memory (PMS) functions. Therefore, we constructed a neurodynamic model conforming to the PMS architecture. Our findings reveal that this network not only addresses the challenges of bidirectional retrieval and task-adaptive neuron allocation, but also spontaneously utilize inter-layer information coupling to achieve memory retrieval while enabling efficient memorization through reuse of existing spatial patterns with similar sequences during learning. Based on this network framework, we investigated the impact of connection degradation in different brain regions on both goal-based and contextual retrieval. Moreover, in the PMS model, we discovered that the three-layer network architecture and inter-layer coupling play crucial roles in sequence memory. Overall, the brain-inspired PMS model provides theoretical possibilities for simulating and explaining phenomena in sequence memory. It also demonstrates potential for achieving complex cognitive functions through implementation of deeper structural hierarchies, thereby inspiring next-generation sequence memory algorithms based on neurodynamic neural networks.
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