Tian Yang, Tan Zeren, Hou Hedong, Li Guoqi, Cheng Aohua, Qiu Yike, Weng Kangyu, Chen Chun, Sun Pei
Department of Psychology & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China.
Laboratory of Advanced Computing and Storage, Central Research Institute, 2012 Laboratories, Huawei Technologies Co. Ltd., Beijing, China.
Netw Neurosci. 2022 Oct 1;6(4):1148-1185. doi: 10.1162/netn_a_00269. eCollection 2022.
Criticality is hypothesized as a physical mechanism underlying efficient transitions between cortical states and remarkable information-processing capacities in the brain. While considerable evidence generally supports this hypothesis, nonnegligible controversies persist regarding the ubiquity of criticality in neural dynamics and its role in information processing. Validity issues frequently arise during identifying potential brain criticality from empirical data. Moreover, the functional benefits implied by brain criticality are frequently misconceived or unduly generalized. These problems stem from the nontriviality and immaturity of the physical theories that analytically derive brain criticality and the statistic techniques that estimate brain criticality from empirical data. To help solve these problems, we present a systematic review and reformulate the foundations of studying brain criticality, that is, ordinary criticality (OC), quasi-criticality (qC), self-organized criticality (SOC), and self-organized quasi-criticality (SOqC), using the terminology of neuroscience. We offer accessible explanations of the physical theories and statistical techniques of brain criticality, providing step-by-step derivations to characterize neural dynamics as a physical system with avalanches. We summarize error-prone details and existing limitations in brain criticality analysis and suggest possible solutions. Moreover, we present a forward-looking perspective on how optimizing the foundations of studying brain criticality can deepen our understanding of various neuroscience questions.
临界性被假定为大脑皮层状态之间有效转换以及大脑卓越信息处理能力背后的一种物理机制。虽然大量证据总体上支持这一假设，但关于临界性在神经动力学中的普遍性及其在信息处理中的作用，仍存在不可忽视的争议。在从经验数据中识别潜在的大脑临界性时，有效性问题经常出现。此外，大脑临界性所隐含的功能益处常常被误解或过度概括。这些问题源于从分析上推导大脑临界性的物理理论以及从经验数据中估计大脑临界性的统计技术的复杂性和不成熟性。为帮助解决这些问题，我们进行了系统综述，并使用神经科学术语重新阐述了研究大脑临界性的基础，即普通临界性（OC）、准临界性（qC）、自组织临界性（SOC）和自组织准临界性（SOqC）。我们对大脑临界性的物理理论和统计技术进行了通俗易懂的解释，提供了逐步推导，以将神经动力学表征为一个具有雪崩的物理系统。我们总结了大脑临界性分析中容易出错的细节和现有局限性，并提出了可能的解决方案。此外，我们对优化大脑临界性研究基础如何能加深我们对各种神经科学问题的理解提出了前瞻性观点。
