A Comparative Framework for Modeling the Low-Dimensional Geometry of Neural Population States

神经群体状态低维几何建模的比较框架

• 批准号: 10007243
• 负责人: Eva Dyer
• 金额: $ 114.27万
• 依托单位: GEORGIA INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-16 至 2024-09-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10007243
• 关键词: Address; Animals; Architecture; Area; Automobile Driving; BRAIN initiative; Back; Behavior; Behavioral; Brain; Code; Collaborations; Communities; Complex; Data; Data Set; Development; Dimensions; Disease; Environment; Esthesia; Formulation; Foundations; Geometry; Health; Knowledge; Learning; Link; Machine Learning; Mathematics; Measurement; Measures; Methods; Modeling; Neurons; Neurophysiology - biologic function; Neurosciences; Pattern; Perception; Population; Population Dynamics; Positioning Attribute; Problem Solving; Role; Rotation; Sampling; Sensory; Shapes; Sleep; Space Models; Structure; System; Technology; Testing; Time; Translations; Vision; Visual Cortex; Withdrawal; addiction; base; cell type; clinically relevant; comparative; computational neuroscience; deep learning; density; deprivation; high dimensionality; innovation; learning network; learning strategy; lens; monocular deprivation; nervous system disorder; neural circuit; neural model; novel; relating to nervous system; response; statistics; temporal measurement; theories; tool

Project Summary Advances in neural recording technology now provide access to neural activity at high temporal resolutions, from many brain areas, and during complex and naturalistic behavior. Interpreting these types of high-dimensional and unconstrained neural recordings is still a major challenge in neuroscience. The aim of this project is to develop innovative methods for distilling high-dimensional neural activity patterns into simpler low-dimensional formats that can be effectively compared across time, conditions, or even across species. Our team is uniquely positioned to not only develop these novel methods, but also apply them to characterize changes in neural systems across a wide range of clinically-relevant perturbations, including addiction, sensory manipulation, and disease. In this project, theory, methods, and models will be developed for: 1) learning low-dimensional latent space models that align many neural datasets onto a common reference frame for comparison, 2) comparing datasets and testing the impact of a variety of perturbations (e.g. monocular deprivation, addiction and withdrawal) on the shape or geometry of neural activity from its baseline state, and 3) investigating the role of specific cell types and microcircuits on shaping population activity over time, during sleep, and in response to certain classes of perturbations. This project will provide new tools and frameworks for comparing neural datasets, leading to robust measures of disease, signatures of addiction, and other network-level reflections of environment and behavior. Significance: As neural datasets continue to grow in size, new methods for analysis are becoming of utmost importance in driving scientific understanding of the brain. The methods developed in this proposal will identify new ways to learn network-level signatures that allow us to link and compare different neural activity patterns. A robust ability to compare activity across time and animals will have wide reaching impacts, and provide new tools to advance network-level understanding of disease. Innovation: This project will leverage state-of-the-art approaches in high-dimensional statistics and geometry, which are simultaneously advancing in the context of deep learning (DL) architectures, and to tackle challenges in neural coding. This project represents a truly innovative combination of tools in machine learning and computational neuroscience which will likely transfer knowledge in both directions: from machine learning to neuroscience and back. The unique application of advanced mathematical tools in geometry and optimization to population-level analysis of perturbations will be transformative, not only for neuroscience but also in the study of DL architectures.

项目摘要 现在 许多大脑区域以及复杂和自然主义的行为。解释这些类型的高维 在神经科学中，无限制的神经记录仍然是一个主要挑战。这个项目的目的是 开发创新的方法，用于将高维神经活动模式提炼成更简单的低维度 可以在时间，条件甚至跨物种之间有效比较的格式。我们的团队是独特的 定位不仅可以开发这些新颖的方法，还可以将它们应用于神经的变化来表征 各种临床上与临床相关的扰动的系统，包括成瘾，感觉操纵和 疾病。在这个项目中，将开发理论，方法和模型：1）学习低维潜在 将许多神经数据集对齐到常见参考框架的空间模型，2）比较 数据集并测试各种扰动的影响（例如，单眼剥夺，成瘾和 提取）从基线状态的神经活动的形状或几何形状，以及3）研究的作用 特定的细胞类型和微电路，以随着时间的流逝，睡眠和对响应 某些类别的扰动。该项目将为比较神经提供新的工具和框架 数据集，导致疾病，成瘾的签名以及其他网络级别的反射 环境和行为。意义：随着神经数据集的规模不断增长，新的分析方法 在推动对大脑的科学理解方面，最重要的是。在 该建议将确定学习网络级签名的新方法，使我们能够链接和比较不同 神经活动模式。在跨时间进行比较活动的强大能力，动物的范围很广 影响并提供新的工具，以提高网络级别对疾病的了解。创新：这个项目将 利用高维统计和几何形状的最新方法，它们同时也是 在深度学习（DL）体系结构的背景下发展，并应对神经编码中的挑战。这 项目代表了机器学习和计算神经科学中工具的真正创新组合 这可能会在这两个方向上转移知识：从机器学习到神经科学和背部。这 高级数学工具在几何和优化中的独特应用到人口级分析 扰动将是变革性的，不仅对于神经科学，而且在研究DL体系结构中。