用流形与能量函数模拟大脑记忆巩固，让人工系统更像人脑。
Neural Manifolds and Cognitive Consistency: A New Approach to Memory Consolidation in Artificial Systems
- 用低维流形表示神经活动，捕捉记忆漂移规律。
- 引入平衡能量函数，使突触连接更连贯，提升可解释性。
- 适合研究类脑智能、神经形态计算的学者参考。
我们提出一种新的数学框架，统一神经种群动力学、海马体尖波涟漪（SpWR）生成以及受海德格尔理论启发的认知一致性约束。模型利用低维流形表示来捕捉结构化的神经漂移，并引入平衡能量函数以强制实现一致的突触交互，有效模拟了生物系统中的记忆巩固过程。仿真结果表明，该方法不仅能复现SpWR事件的关键特征，还提升了网络的可解释性。本工作为连接神经科学与人工智能的可扩展神经形态架构铺平道路，为未来智能系统提供更鲁棒、自适应的学习机制。
原文摘要 · Abstract (English)
We introduce a novel mathematical framework that unifies neural population dynamics, hippocampal sharp wave-ripple (SpWR) generation, and cognitive consistency constraints inspired by Heider's theory. Our model leverages low-dimensional manifold representations to capture structured neural drift and incorporates a balance energy function to enforce coherent synaptic interactions, effectively simulating the memory consolidation processes observed in biological systems. Simulation results demonstrate that our approach not only reproduces key features of SpWR events but also enhances network interpretability. This work paves the way for scalable neuromorphic architectures that bridge neuroscience and artificial intelligence, offering more robust and adaptive learning mechanisms for future intelligent systems.
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