AI 中文总结
本研究提出利用Fisher信息度量作为无模型工具，通过其峰值宽度和高度连续量化神经系统接近临界性的程度，无需知道控制参数。
AI 中文摘要
临界现象在许多学科中广泛存在，并且最近已成为生物和人工神经网络研究中的一个深入关注的话题。临界性的一个显著特征是出现具有幂律分布大小和持续时间的雪崩。然而，经验估计临界指数仍然具有挑战性，且其解释往往依赖于模型。在这项工作中，我们展示了Fisher信息度量（FIM），一种广义敏感性的度量，如何为神经系统中的临界区域提供全面的、模型无关的表征。我们在一系列生物复杂性递增的模型中验证了这种方法，从典型的分支过程到尖峰模型和全脑模型，表明每个模型控制参数的FIM可靠地追踪系统的临界程度。为了模拟对真实世界系统的研究，其中控制参数未知，我们额外计算了观察到的神经活动分支比的FIM。由此产生的FIM在活动增长和衰减平衡处达到峰值，且随着系统接近临界性，峰值变得更加尖锐。因此，FIM的峰值宽度和高度提供了对临界性接近程度的连续、无模型的读数，而无需知道真实控制参数，为探测神经系统中的临界性提供了一个强大的工具。
英文摘要
Critical phenomena are widespread across many disciplines and have recently become a topic of deep interest in the study of biological and artificial neural networks. A distinct signature of criticality is the emergence of avalanches with power-law-distributed sizes and durations. However, empirically estimating the critical exponents remains challenging, and their interpretation is often model-dependent. In this work, we demonstrate how the Fisher Information Metric (FIM), a measure of generalized susceptibility, provides a comprehensive, model-agnostic characterization of the critical region in neural systems. We validate this approach across models of increasing biological complexity, from prototypical branching processes to spiking and whole-brain models, showing that FIM of each model's control parameter reliably tracks the system's degree of criticality. To emulate the study of real-world systems, where the control parameter is unknown, we additionally calculate FIM of the observed branching ratio of neural activity. The resulting FIM peaks where activity growth and decay balance, with the peak sharpening as the system approaches criticality. Hence, FIM peak width and height yield continuous, model-free readouts of proximity to criticality without requiring knowledge of the true control parameter, offering a robust tool for probing criticality in neural systems.
