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 神经记录技术的进步现在提供了高时间分辨率的神经活动， 许多大脑区域，以及复杂和自然的行为。解释这些类型的高维 不受约束的神经记录仍然是神经科学的主要挑战。该项目的目的是 开发创新方法，将高维神经活动模式提取为更简单的低维神经活动模式。 可以跨时间、条件甚至跨物种进行有效比较的格式。我们的团队是独一无二的 定位不仅开发这些新的方法，而且还将其应用于表征神经系统的变化， 系统在广泛的临床相关的扰动，包括成瘾，感觉操纵， 疾病本课题主要研究以下几个方面的理论、方法和模型：1）学习低维潜在 将许多神经数据集对齐到公共参考系上进行比较的空间模型，2）比较 数据集和测试各种干扰的影响（例如单眼剥夺，成瘾和 撤回）对神经活动的形状或几何结构从其基线状态，和3）调查的作用， 特定的细胞类型和微电路在塑造人口活动随着时间的推移，在睡眠期间，并在响应 某些类型的扰动。该项目将提供新的工具和框架， 数据集，导致疾病的强大措施，成瘾的签名，以及其他网络层面的反映， 环境和行为。重要性：随着神经数据集规模的不断增长，新的分析方法 在推动对大脑的科学理解方面变得至关重要。开发的方法 该提案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的 神经活动模式一个强大的能力，比较活动跨时间和动物将有广泛的影响 影响，并提供新的工具，以促进网络层面的疾病的理解。创新：该项目将 利用最先进的方法在高维统计和几何，这是同时 在深度学习（DL）架构的背景下推进，并应对神经编码的挑战。这 该项目代表了机器学习和计算神经科学工具的真正创新组合 这可能会在两个方向上传递知识：从机器学习到神经科学，然后再回来。的 独特的应用先进的数学工具，在几何和优化人口水平的分析， 扰动将是变革性的，不仅对神经科学，而且在DL架构的研究。