CIF: Medium: Collaborative Research: Scalable Learning of Nonlinear Models in Large Neural Populations

CIF：媒介：协作研究：大型神经群体中非线性模型的可扩展学习

• 批准号: 1738286
• 负责人: Alyson Fletcher
• 金额: $ 40万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1738286&HistoricalAwards=false
• 关键词: CIF; Medium; Collaborative; Research; Scalable

Fundamental to understanding information processing in the brain are methods that can systematically characterize the structure and dynamics of neural circuits that underlie perception and cognition. Micro- electrocorticography (µECoG) is the practice of using microelectrodes placed directly on the exposed surface of the brain to record electrical activity from the cerebral cortex. Recent advances in µECoG provide unique opportunities to observe large regions of the neural cortex at unprecedented spatial and temporal resolution. However, uncovering the structure of complex neural circuits is challenging. This interdisciplinary project develops methods for learning high-dimensional nonlinear systems with a particular focus on these systems as they arise in cortical networks and validates these techniques on state-of-the-art µECoG systems.Three thrusts are considered: The first considers the general problem of state estimation in high-dimensional dynamical systems using decomposition methods including distributed Kalman and particle filtering and graphical models. The main goal is to provide computationally scalable and flexible approaches with provable guarantees. The second combines these state estimation methods with Bayesian parameter estimation and compressed sensing techniques to identify connectivity and nonlinear dynamics in the networks. The third validates these methods on identification of neural models from µECoG arrays. Applications to neural mapping, auditory and visual stimuli decoding are explored. In particular, the project seeks to demonstrate the method on using recordings from rat primary auditory cortex and cat visual cortex using a novel, flexible, high-resolution electrode array.

理解大脑信息处理的基础是可以系统地表征基于感知和认知的神经回路的结构和动力学的方法。微皮质造影（µECOG）是使用直接放在脑裸露表面上的微电极的实践，以记录大脑皮层的电活动。 µECOG的最新进展为观察空前的空间和临时分辨率观察大型神经元皮质区域提供了独特的机会。但是，挑战了复杂神经元电路的结构。这个跨学科项目开发了学习高维非线性系统的方法，特别关注这些系统，因为它们在皮质网络中出现，并验证这些技术对最先进的µECOG系统进行了验证。考虑了三个推力：首先考虑了使用分配方法和分布式kal的高维动力学系统中国家估计的一般性估计问题和粒子孔径和分布式的粒子和粒子孔和粒子的估算。主要目标是提供具有可证明保证的计算可扩展和灵活的方法。第二种将这些状态估计方法与贝叶斯参数估计和压缩灵敏度技术结合在一起，以识别网络中的连接性和非线性动力学。第三个验证了这些方法关于识别μecog阵列的神经模型的方法。探索了神经膜，听觉和视觉刺激解码的应用。特别是，该项目试图证明使用新颖的，柔性的高分辨率电极阵列使用大鼠一级听觉皮层和CAT Visual Cortex的记录的方法。