状态空间模型自然产生时间细胞与振荡行为，并可扩展至抽象认知功能
State Space Models Naturally Produce Time Cell and Oscillatory Behaviors and Scale to Abstract Cognitive Functions
该预印本提出，基于状态空间模型的数学框架足以生成类似时间细胞、斜坡活动和振荡反应的响应模式。作者报告，这些行为可从结构化的多时间尺度复旋转基中自然涌现，该基在训练前即产生结构化时间动态，学习过程则利用并适配这一既有时间基以完成特定任务；模型还可扩展至事件计数等抽象认知功能。研究为理解神经活动提供了可计算框架，但结果来自模型模拟，尚需生物实验验证。预印本未完成同行评审。
arXiv:2507.13638v4 Announce Type: replace Abstract: A grand challenge in modern neuroscience is bridging the gap between the microscale neural circuits and cognitive functions. While extensive knowledge exists about neuronal connectivity and biophysics, how low-level phenomena give rise to abstract behaviors remains largely unresolved. Here, we propose that a mathematical framework based on State Space Models offers mathematical sufficiency and computational plausibility to generate response profiles that resemble time cells, ramping activity, and oscillatory responses. Our results suggest that such behaviors can emerge from a structured multi-timescale complex rotational basis, which enables optimized history compression and naturally generates structured temporal dynamics even prior to training, mirroring recent findings in biological circuits. Learning exploits and adapts to the pre-existing temporal basis for specific tasks. We show that the model can readily scale to abstract cognitive functions such as event counting, supporting its use as a computationally tractable framework for understanding neural activities.
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来源：arXiv q-bio.NC 预印本 · arxiv.org