Coarse-graining approaches to networks, learning, and behavior

项目摘要 本提案中提出的理论中心将致力于将成功和强大的方法转化为 描述物理系统中涌现的集体行为，这样就可以应用于大脑。工作 这三位理论家将紧密合作，开发出寻找和量化相关模式的方法， 大脑中的群体活动，包括活动的瞬时快照和随着时间的推移而演变的活动。 方法将在不同处理阶段和尺度的广泛神经系统中进行测试： 从蝾螈到啮齿动物再到人类，从视网膜到皮层，从几十到几千个细胞。的方法 将通过检查神经代码可以高保真度读出来验证，即使在 压缩到一个更小的子空间里该项目将产生数据分析代码， 可供神经科学研究人员使用自己的数据，除了分析的结果， 研究的特定系统。 神经代码本质上是集体的;虽然单个神经元执行复杂的计算，但数百个神经元执行复杂的计算。 即使在最简单的生物体中，也有成千上万的神经元被用来感知环境和驱动行为。 尽管过去的一百年在神经科学方面取得了实质性的进展，但直到最近， 研究人员有能力从完整的神经群体中记录-也就是说，观察集体 一个正常运作的神经网络的行为。随着这些快速的实验进展，迫切需要 互补的理论和计算方法，以指导探索紧急行为， 大群的神经元，使人们能够把“大数据”变成“大想法”。该提案概述了一条通往 发现和定量分析大脑中集体现象的新理论框架， 是感官编码、空间表征、预测和最终驱动行为的基础。项目 在很大程度上借鉴了理论物理学中所谓的重整化群方法的成功， 彻底改变了对物理系统中集体现象的理解， 世纪后半叶统计物理学的进展。本研究中探索的方法 建议推广了这些技术，使它们可以应用于更广泛问题。 该理论中心基于重整化群开发的方法将适用于广泛的 一系列的神经数据，因为它们被明确地设计为将技术从理论物理推广到 更广泛的背景。事实上，这种方法的一个更大的目标是寻找集体行为的普遍性， 神经密码所提出的技术执行起来相对简单， 一种基本的方法，用于在行为和行为等不同领域询问高维数据， 神经科学和生物物理学。这些新技术也将作为三位理论家正在进行的 努力让即将进入生物科学的研究生接触生物学中的定量方法。

Project Summary The theory hub put forward in this proposal will work to translate successful and powerful approaches to describing emergent collective behavior in physical systems so they can be applied to the brain. Working closely together, the three theorists will develop methods for finding and quantifying the relevant modes of population activity in the brain, both in instantaneous snapshots of activity and activity as it evolves in time. Methods will be tested in a wide range of neural systems at different processing stages and scales: from salamanders to rodents to humans, from the retina to the cortex, from tens to thousands of cells. The approach will be validated by checking that the neural code can be read out with high fidelity even after being compressed into a much smaller subspace. The project will produce data analysis code that will be made available for neuroscience researchers to use on their own data, in addition to the results of the analyses of the particular systems studied. The neural code is inherently collective; while single neurons execute sophisticated computations, hundreds to thousands of neurons are utilized to sense the environment and drive behavior in even the simplest organisms. Although the past hundred years have yielded substantial progress in neuroscience, only recently have researchers had the capacity to record from complete neural populations - that is, to view the collective behavior of a functioning neural network. With these rapid experimental advances, there is an urgent need for complementary theoretical and computational approaches to guide the exploration of emergent behavior in large groups of neurons, allowing one to turn `big data' into `big ideas'. This proposal outlines a path towards a new theoretical framework for finding and quantitatively analyzing collective phenomena in the brain that underlie sensory coding, the representation of space, prediction, and ultimately drive behavior. The project draws heavily on the success of so-called renormalization group approaches in theoretical physics that revolutionized the understanding of collective phenomena in physical systems, and sculpted much of the progress in statistical physics in the second half of the twentieth century. The methods explored in this proposal generalize such techniques so they can be applied to a much wider range of problems. The methods developed by this theory hub based on the renormalization group will be applicable to a wide range of neural data since they are explicitly designed to generalize techniques from theoretical physics to a much broader setting. Indeed, a larger goal of the approach is to search for universality in collective behavior in the neural code. The techniques proposed are relatively straightforward to execute and will provide a fundamental methodology for interrogating high-dimensional data in fields as diverse as behavioral neuroscience and biophysics. The new techniques will also be taught as part of the three theorists' ongoing efforts to expose incoming graduate students in biological sciences to quantitative methods in biology.