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: 1564142
• 负责人: Sundeep Rangan
• 金额: $ 39.99万
• 依托单位: New York University
• 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 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1564142&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能够系统地描述构成感知和认知基础的神经回路的结构和动力学的方法。微型皮层脑电图术是使用直接放置在大脑裸露表面的微电极来记录大脑皮层的电活动的实践。µECoG的最新进展为以前所未有的空间和时间分辨率观察大片神经皮质提供了独特的机会。然而，揭开复杂神经电路的结构是具有挑战性的。这个跨学科的项目开发了学习高维非线性系统的方法，特别关注这些系统在大脑皮层网络中出现的情况，并在最先进的µECoG系统上验证了这些技术。考虑了三个方面：第一个考虑了使用分解方法(包括分布式卡尔曼和粒子滤波)和图形模型的高维动态系统的一般状态估计问题。其主要目标是提供具有可证明保证的计算可伸缩和灵活的方法。第二种方法将这些状态估计方法与贝叶斯参数估计和压缩感知技术相结合，以识别网络中的连通性和非线性动力学。第三个实验验证了这些方法在µECoG阵列神经模型识别中的有效性。探索了在神经映射、听觉和视觉刺激解码方面的应用。特别是，该项目试图演示使用来自大鼠初级听觉皮质和猫视觉皮质的记录的方法，使用一种新颖、灵活、高分辨率的电极阵列。