It has been hypothesized that the brain optimizes its capacity for computation by self-organizing to a critical point. The dynamical state of criticality is achieved by striking a balance such that activity can effectively spread through the network without overwhelming it and is commonly identified in neuronal networks by observing the behavior of cascades of network activity termed “neuronal avalanches.” The dynamic activity that occurs in neuronal networks is closely intertwined with how the elements of the network are connected and how they influence each other's functional activity. In this review, we highlight how studying criticality with a broad perspective that integrates concepts from physics, experimental and theoretical neuroscience, and computer science can provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders. First, integrating graph theory into experimental studies on criticality, as is becoming more common in theoretical and modeling studies, would provide insight into the kinds of network structures that support criticality in networks of biological neurons. Furthermore, plasticity mechanisms play a crucial role in shaping these neural structures, both in terms of homeostatic maintenance and learning. Both network structures and plasticity have been studied fairly extensively in theoretical models, but much work remains to bridge the gap between theoretical and experimental findings. Finally, information theoretical approaches can tie in more concrete evidence of a network's computational capabilities. Approaching neural dynamics with all these facets in mind has the potential to provide a greater understanding of what goes wrong in neural disorders. Criticality analysis therefore holds potential to identify disruptions to healthy dynamics, granted that robust methods and approaches are considered.

假设大脑通过自我组织到临界点来优化其计算能力。动态的临界状态是通过达到一种平衡来实现的，从而使活动可以有效地在整个网络中传播而不会使其不堪重负，并且通常在神经元网络中通过观察网络活动级联的行为（称为“神经雪崩”）来识别。神经元网络中发生的动态活动与网络元素如何连接以及它们如何影响彼此的功能活动紧密相关。在这篇评论中，我们重点介绍如何以广泛的视角研究临界性，将物理，实验和理论神经科学的概念融为一体，计算机科学可以使人们更深入地了解将网络驱动到关键程度的机制，以及它们在不同疾病中如何表现出来的破坏。首先，将图论整合到关于临界性的实验研究中，正如在理论和建模研究中越来越常见的那样，将提供对支持生物神经元网络中的临界性的网络结构的深入了解。此外，就稳态维持和学习而言，可塑性机制在塑造这些神经结构中起着至关重要的作用。在理论模型中已经对网络结构和可塑性进行了相当广泛的研究，但是仍然有许多工作可以弥合理论和实验结果之间的差距。最后，信息理论方法可以结合网络的更具体证据 的计算能力。牢记所有这些方面来研究神经动力学有可能提供对神经疾病出了什么问题的更好的了解。因此，如果考虑了健壮的方法和方法，则临界分析具有识别健康动力的潜力。