In this review, we have attempted to sketch the outlines of a new interdisciplinary field, which we call network neuroscience. The field gathers momentum as networks have become ubiquitous phenomena encountered in empirical investigation as well as computational analysis and modeling of neurobiological systems at all scales. The ever-growing volume of big data in neuroscience demands not only advanced analytics and sound statistical inference, but it also calls for theoretical ideas that can unify our understanding of brain structure and function. Theory is indispensable, as it allows us to transform big data into ‘small data’ and, ultimately, knowledge—delivering compact descriptions of
regularities, principles and laws that apply to the architecture and functioning of neural systems. We believe that network neuroscience can make an important contribution toward unifying an otherwise fractured discipline by providing a common conceptual framework and a common tool set to meet the challenges of modern neuroscience. Network neuroscience naturally connects with other important theoretical approaches such as dynamical systems, neural coding and statistical physics.在这篇综述中，我们试图勾勒出一个新的跨学科领域的轮廓，我们称之为网络神经科学。随着网络在各种规模的神经生物学系统的实证研究、计算分析和建模中成为普遍存在的现象，这一领域的发展势头越来越大。神经科学中不断增长的大数据量不仅需要先进的分析和可靠的统计推断，还需要能够统一我们对大脑结构和功能的理解的理论观点。理论是必不可少的，因为它使我们能够将大数据转换为“小数据”，并最终提供适用于神经系统架构和功能的规律性、原则和规律的简洁描述。我们相信，网络神经科学可以通过提供一个共同的概念框架和一套共同的工具来应对现代神经科学的挑战，从而为统一一个原本支离破碎的学科做出重要贡献。网络神经科学自然地与动力系统、神经编码和统计物理等其他重要的理论方法相联系。（摘自，同上）