Departamento de Electromagnetismo y Física de la Materia, Instituto Carlos I de Física Teórica y Computacional Universidad de Granada, Granada E-18071, Spain.
Morton B. Zuckerman Mind Brain Behavior Institute Columbia University, New York, NY 10027.
Proc Natl Acad Sci U S A. 2023 Feb 28;120(9):e2208998120. doi: 10.1073/pnas.2208998120. Epub 2023 Feb 24.
The brain is in a state of perpetual reverberant neural activity, even in the absence of specific tasks or stimuli. Shedding light on the origin and functional significance of such a dynamical state is essential to understanding how the brain transmits, processes, and stores information. An inspiring, albeit controversial, conjecture proposes that some statistical characteristics of empirically observed neuronal activity can be understood by assuming that brain networks operate in a dynamical regime with features, including the emergence of scale invariance, resembling those seen typically near phase transitions. Here, we present a data-driven analysis based on simultaneous high-throughput recordings of the activity of thousands of individual neurons in various regions of the mouse brain. To analyze these data, we construct a unified theoretical framework that synergistically combines a phenomenological renormalization group approach and techniques that infer the general dynamical state of a neural population, while designing complementary tools. This strategy allows us to uncover strong signatures of scale invariance that are "quasiuniversal" across brain regions and experiments, revealing that all the analyzed areas operate, to a greater or lesser extent, near the edge of instability.
大脑处于持续的回荡神经活动状态，即使在没有特定任务或刺激的情况下也是如此。阐明这种动态状态的起源和功能意义对于理解大脑如何传输、处理和存储信息至关重要。一个令人鼓舞的、尽管有争议的假设提出，通过假设大脑网络在具有特征的动力学状态下运行，可以理解经验观察到的神经元活动的一些统计特征，包括出现标度不变性，类似于在典型的相变附近看到的那些特征。在这里，我们提出了一种基于同时对小鼠大脑的不同区域中数千个单个神经元的活动进行高通量记录的数据分析。为了分析这些数据，我们构建了一个统一的理论框架，该框架协同结合了一种现象学重整化群方法和推断神经群体一般动力学状态的技术，同时设计了互补工具。这种策略使我们能够揭示出跨越大脑区域和实验的强标度不变性特征，这些特征是“准普遍”的，这表明所有分析的区域都在不同程度上接近不稳定性的边缘。
