Heiney Kristine, Huse Ramstad Ola, Fiskum Vegard, Christiansen Nicholas, Sandvig Axel, Nichele Stefano, Sandvig Ioanna
Department of Computer Science, Oslo Metropolitan University, Oslo, Norway.
Department of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Front Comput Neurosci. 2021 Feb 10;15:611183. doi: 10.3389/fncom.2021.611183. eCollection 2021.
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.
有人提出假说，认为大脑通过自组织达到临界点来优化其计算能力。临界状态是通过达成一种平衡来实现的，即活动能够在网络中有效传播而不会使其不堪重负，并且在神经网络中，通常通过观察被称为“神经元雪崩”的网络活动级联行为来识别这种状态。神经网络中发生的动态活动与网络元素的连接方式以及它们如何相互影响功能活动密切相关。在这篇综述中，我们强调，从物理学、实验和理论神经科学以及计算机科学等多学科综合的广泛视角来研究临界性，能够更深入地理解驱动网络达到临界状态的机制，以及这些机制的破坏在不同疾病中可能如何表现。首先，正如在理论和建模研究中越来越常见的那样，将图论整合到关于临界性的实验研究中，将有助于深入了解支持生物神经元网络临界性的网络结构类型。此外，可塑性机制在塑造这些神经结构方面起着至关重要的作用，无论是在稳态维持还是学习方面。网络结构和可塑性在理论模型中都已经得到了相当广泛的研究，但在弥合理论和实验结果之间的差距方面仍有许多工作要做。最后，信息理论方法可以提供有关网络计算能力的更具体证据。综合考虑所有这些方面来研究神经动力学，有可能更深入地理解神经疾病中出现的问题。因此，只要考虑到稳健的方法和途径，临界性分析就有可能识别对健康动态的破坏。
