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arXiv 2601.04450nlin.AOphysics.comp-ph
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受保护的平均场动力学中的自组织临界性:反射神经系统的环稳定性与内部重整化

Self-Organized Criticality from Protected Mean-Field Dynamics: Loop Stability and Internal Renormalization in Reflective Neural Systems

Electronics and Telecommunications Research Institute(电子电信研究所)

Byung Gyu Chae

AI总结:

本文通过反射稳态动力学机制,揭示了神经系统中自组织临界性的保护性平均场临界表面,并展示了响应驱动的结构适应如何吸引系统向该表面,无需外部微调。

摘要:

反射稳态动力学提供了一种神经系统中自组织临界性的最小机制。从简化的随机描述出发,在MSRJD场论框架内,我们证明波动效应不会破坏临界流形。相反,环状修正通过稳态曲率动态地被重整化,产生一个受保护的平均场临界表面,在粗粒化下仍保持边缘稳定。除了鲁棒性外,我们还表明响应驱动的结构适应会产生内在参数流,将系统吸引向该表面而无需外部微调。这些结果在一个框架内统一了环重整化和适应性响应,并为重新进入神经动力学中的自主临界性建立了具体路径。

英文摘要:

The reflective homeostatic dynamics provides a minimal mechanism for self-organized criticality in neural systems. Starting from a reduced stochastic description, we demonstrate within the MSRJD field-theoretic framework that fluctuation effects do not destabilize the critical manifold. Instead, loop corrections are dynamically regularized by homeostatic curvature, yielding a protected mean-field critical surface that remains marginally stable under coarse-graining. Beyond robustness, we show that response-driven structural adaptation generates intrinsic parameter flows that attract the system toward this surface without external fine tuning. Together, these results unify loop renormalization and adaptive response in a single framework and establish a concrete route to autonomous criticality in reentrant neural dynamics.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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