发表机构
Yale University（耶鲁大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究表明，随着神经元数量增加，临界性无需精细调节即可自然涌现，通过正问题和逆问题的映射关系，模型集中于临界点，并在小鼠大脑记录中验证了向临界性的流动，解决了临界性与精细调节之间的张力。
AI 中文摘要
临界系统位于性质截然不同的行为之间的边界附近。在推断神经活动模型时，这种接近临界性的特性被认为需要精确的参数调节。在此，我们表明随着神经元数量的增加，临界性可以在无需精细调节的情况下自然涌现。当从参数计算可观测统计量（正问题）时，参数空间中的一些小区块映射到统计空间中的大区块。这些特殊参数恰好是接近临界性的那些参数。因此，当从实验测量推断参数（逆问题）时，模型集中在临界点附近，并且这种集中随着系统增大而增强。我们通过小鼠大脑中的多个大规模记录展示了这种向临界性的流动。在伊辛自旋的居里-外斯模型中，我们发现尽管底层系统存在显著差异，所有记录都坍缩到一级相变。这些结果共同表明，在神经活动模型中，临界性与精细调节之间的张力得到了解决。
英文摘要
Critical systems sit near boundaries between qualitatively distinct behaviors. When inferring models of neural activity, this proximity to criticality is thought to require the precise tuning of parameters. Here, we show that as the number of neurons increases, criticality can emerge naturally without fine-tuning. When computing observable statistics from parameters (the forward problem), some small regions in parameter space map to large regions in statistics space. These special parameters are precisely those near criticality. Thus, when inferring parameters from experimental measurements (the inverse problem), models concentrate near critical points, and this concentration becomes stronger as the system grows. We illustrate this flow toward criticality across many large-scale recordings in the mouse brain. In the Curie-Weiss model of Ising spins, we find that all of the recordings collapse to a first-order phase transition, despite substantial differences in the underlying systems. Together, these results suggest a resolution to the tension between criticality and fine-tuning in models of neural activity.
