Coarse-graining approaches to networks, learning, and behavior

网络、学习和行为的粗粒度方法

• 批准号: 10002224
• 负责人: WILLIAM BIALEK
• 金额: $ 35.35万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-20 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10002224
• 关键词: Affect; Animal Behavior; Area; Behavior; Behavioral; Big Data; Bioinformatics; Biological Sciences; Biology; Biophysics; Brain; Cells; Code; Communities; Complement; Complex; Data; Data Analyses; Dimensions; Entropy; Environment; Event; Exposure to; Eye Movements; Formulation; Freedom; Goals; Grain; Hippocampus (Brain); Human; Intuition; Maps; Methodology; Methods; Modeling; Motion; Neurons; Neurophysiology - biologic function; Neurosciences; Organism; Physics; Play; Population; Primates; Procedures; Process; Property; Psychological Techniques; Research Personnel; Retina; Retinal Ganglion Cells; Rodent; Salamander; Sensory; Shapes; Statistical Mechanics; Structure; System; Techniques; Testing; Time; Translating; Work; base; design; driving behavior; fly; graduate student; learned behavior; movie; multidimensional data; neural network; preservation; relating to nervous system; senior faculty; statistics; success; symposium; theories

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.

项目摘要 该提案中提出的理论中心将有效地将成功有力的方法转化为 描述物理系统中新兴的集体行为，以便将它们应用于大脑。在职的 紧密地在一起，三位理论家将开发寻找和量化相关模式的方法 随着时间的流逝，活动和活动的瞬时快照中的人群活动。 方法将在不同的处理阶段和尺度的广泛神经系统中进行测试： 从视网膜到皮质，从数十到成千上万的细胞，从视网膜到皮质的人类到啮齿动物。方法 即使在 压缩成小得多的子空间。该项目将生成将制定的数据分析代码 除了分析的结果外，可供神经科学研究人员使用自己的数据 研究的特定系统。 神经代码本质上是集体的。单个神经元执行复杂的计算，而数百个 即使是最简单的生物，数以千计的神经元被用来感知环境和推动行为。 尽管过去一百年在神经科学方面取得了重大进展，但直到最近才 研究人员有能力从完整的神经种群中记录记录 - 也就是说，查看集体 功能性神经网络的行为。随着这些快速的实验进步，迫切需要 互补的理论和计算方法指导探索新兴行为 大量神经元，使人们可以将“大数据”变成“大想法”。该提议概述了通往 用于查找和定量分析大脑中集体现象的新理论框架 感官编码，空间，预测的表示以及最终驱动行为的基础。项目 在理论物理学中所谓的重新归一化群体方法的成功借鉴了理论物理学的成功 彻底改变了对物理系统中集体现象的理解，并雕刻了许多 二十世纪下半叶统计物理学的进展。在此探索的方法 建议将这种技术推广，以便将它们应用于更广泛的问题。 该理论中心开发的基于重归其化组的方法将适用于广泛的 神经数据的范围是因为它们的明确设计是为了将技术从理论物理学概括为 更广泛的环境。确实，该方法的更大目标是在集体行为中寻找普遍性 神经法规。提出的技术相对直接执行，并将提供 询问高维数据的基本方法，例如行为的多样化 神经科学和生物物理学。新技术也将作为三位理论家持续的一部分教授 努力使生物科学中的新兴研究生接触生物学的定量方法。