Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA.
Nat Rev Neurosci. 2018 Sep;19(9):566-578. doi: 10.1038/s41583-018-0038-8.
Network theory provides an intuitively appealing framework for studying relationships among interconnected brain mechanisms and their relevance to behaviour. As the space of its applications grows, so does the diversity of meanings of the term network model. This diversity can cause confusion, complicate efforts to assess model validity and efficacy, and hamper interdisciplinary collaboration. In this Review, we examine the field of network neuroscience, focusing on organizing principles that can help overcome these challenges. First, we describe the fundamental goals in constructing network models. Second, we review the most common forms of network models, which can be described parsimoniously along the following three primary dimensions: from data representations to first-principles theory; from biophysical realism to functional phenomenology; and from elementary descriptions to coarse-grained approximations. Third, we draw on biology, philosophy and other disciplines to establish validation principles for these models. We close with a discussion of opportunities to bridge model types and point to exciting frontiers for future pursuits.
网络理论为研究相互关联的大脑机制及其与行为的关系提供了一个直观的框架。随着其应用范围的扩大，网络模型这一术语的含义也变得多样化。这种多样性可能会造成混淆，使评估模型有效性和功效的工作变得复杂，并阻碍跨学科合作。在这篇综述中，我们研究了网络神经科学领域，重点关注有助于克服这些挑战的组织原则。首先，我们描述了构建网络模型的基本目标。其次，我们回顾了最常见的网络模型形式，这些模型可以沿着以下三个主要维度简洁地描述：从数据表示到第一性原理理论；从生物物理现实到功能现象学；从基本描述到粗粒度近似。第三，我们借鉴生物学、哲学和其他学科为这些模型建立了验证原则。最后，我们讨论了弥合模型类型的机会，并指出了未来研究的令人兴奋的前沿。
