PDF(Pubmed)
Abstract：
Systems neuroscience is facing an ever-growing mountain of data. Recent advances in protein engineering and microscopy have together led to a paradigm shift in neuroscience; using fluorescence, we can now image the activity of every neuron through the whole brain of behaving animals. Even in larger organisms, the number of neurons that we can record simultaneously is increasing exponentially with time. This increase in the dimensionality of the data is being met with an explosion of computational and mathematical methods, each using disparate terminology, distinct approaches, and diverse mathematical concepts. Here we collect, organize, and explain multiple data analysis techniques that have been, or could be, applied to whole-brain imaging, using larval zebrafish as an example model. We begin with methods such as linear regression that are designed to detect relations between two variables. Next, we progress through network science and applied topological methods, which focus on the patterns of relations among many variables. Finally, we highlight the potential of generative models that could provide testable hypotheses on wiring rules and network progression through time, or disease progression. While we use examples of imaging from larval zebrafish, these approaches are suitable for any population-scale neural network modeling, and indeed, to applications beyond systems neuroscience. Computational approaches from network science and applied topology are not limited to larval zebrafish, or even to systems neuroscience, and we therefore conclude with a discussion of how such methods can be applied to diverse problems across the biological sciences.
摘要：
系统神经科学正面临着越来越多的数据。蛋白质工程和显微镜的最新进展共同导致了神经科学的范式转变；使用荧光，我们现在可以通过行为动物的整个大脑来成像每个神经元的活动。即使在更大的生物中，我们可以同时记录的神经元数量随着时间呈指数增长。数据维数的这种增加正在遇到计算和数学方法的爆炸式增长，每个都使用不同的术语，不同的方法，和不同的数学概念。在这里，我们收集，组织,并解释了多种数据分析技术，或者可能是，应用于全脑成像，以幼体斑马鱼为例模型。我们从线性回归等方法开始，这些方法旨在检测两个变量之间的关系。接下来,我们通过网络科学和应用拓扑方法进步，它们专注于许多变量之间的关系模式。最后,我们强调了生成模型的潜力，这些模型可以提供关于布线规则和网络随时间进展的可测试假设，或疾病进展。虽然我们使用幼体斑马鱼成像的例子，这些方法适用于任何人口规模的神经网络建模，事实上，超越系统神经科学的应用。来自网络科学和应用拓扑的计算方法不仅限于幼体斑马鱼，甚至是系统神经科学，因此，我们最后讨论了如何将这些方法应用于整个生物科学的各种问题。
