CAREER: Dynamical Systems Modeling of Large-Scale Neural Signals Underlying Cognition

职业：认知基础大规模神经信号的动态系统建模

• 批准号: 1845836
• 负责人: Il Park
• 金额: $ 46.73万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845836&HistoricalAwards=false
• 关键词: CAREER; Dynamical; Systems; Modeling; Large

Simple mathematical expressions can often precisely describe animal behaviors during simple laboratory tasks; however, the specific inner workings of the brain reflected by its complex activity patterns have remained elusive. This project aims to transform the mathematical and computational analysis paradigm for understanding the fundamental organizational principles of neural systems through the development of novel neural data analysis tools. In particular, the computational strategies used by the animals for two behaviors: to perceive if a random visual pattern is moving toward left or right, and to combine information from visual and auditory senses to determine if they are from a potential predator, will be analyzed and visualized as hidden forces acting on the internal brain states from moment to moment. These tools will also be applied to studying disorders of consciousness in coma patients. Successful achievement of these goals will provide new analytical tools to accelerate systems-level understanding of cognition and inspire development of new treatment devices for neurological disorders. The outreach goal of this project is to increase the awareness of the general public on how the nervous system functions through short-form online educational videos that will explore misconceptions and inaccurate analogies made between computer systems and the brain, and thereby stimulate their curiosity and desire to understand how the nervous system functions at different levels. The educational goal of this project is to enrich the computational neuroscience community through curriculum development and engaging high school, undergraduate, and graduate students in the cutting-edge research and trained in advanced quantitative methodologies in computational neuroscience and neural engineering.The goals of this project are to develop both real-time and offline analysis methods to infer the normal and abnormal cognitive dynamics, that is, the internal mental processes that intelligently link sensory and motor function, manifested within the neural systems from streaming neural signals. The neural data analysis techniques will be tailored for extracting interpretable dynamics from neural time series by utilizing the low-dimensional dynamical system structure and recent developments in nonlinear state-space modeling and recurrent neural networks in machine learning. The extracted dynamics and phase portraits are expected to serve as an integral part of human understandable description of how the system operates at an intermediate scale where population state rather than individual neurons describe neural computation. The outcomes of this project will aid the development of translational medicine applications of closed-loop brain stimulation, and the education of the next generation computational neuroscientists.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

简单的数学表达式往往可以精确地描述动物在简单的实验任务中的行为；然而，大脑复杂的活动模式所反映的具体内部运作仍然是难以捉摸的。该项目旨在通过开发新颖的神经数据分析工具，改变数学和计算分析范式，以理解神经系统的基本组织原理。特别是，动物用于两种行为的计算策略：感知随机的视觉模式是向左还是向右移动，以及结合视觉和听觉的信息来确定它们是否来自潜在的捕食者，这些将被分析和可视化为时时刻刻作用于内部大脑状态的隐藏力量。这些工具也将用于研究昏迷患者的意识障碍。这些目标的成功实现将提供新的分析工具，以加速对认知的系统级理解，并激励开发新的神经系统疾病治疗设备。这项计划的外展目标，是透过简短的线上教育影片，探索电脑系统与大脑之间的误解和不准确类比，提高大众对神经系统如何运作的认识，从而激发他们的好奇心和渴望，了解神经系统如何在不同层面运作。该项目的教育目标是通过课程开发和吸引高中、本科生和研究生参与计算神经科学和神经工程领域的前沿研究和先进定量方法的培训，丰富计算神经科学社区。该项目的目标是开发实时和离线分析方法，以推断正常和异常的认知动态，即从流神经信号中体现在神经系统内的智能连接感觉和运动功能的内部心理过程。神经数据分析技术将通过利用低维动态系统结构和机器学习中非线性状态空间建模和循环神经网络的最新发展，从神经时间序列中提取可解释的动态。提取的动态和相位肖像预计将作为人类可理解的系统如何在中间尺度上运行的描述的组成部分，其中群体状态而不是单个神经元描述神经计算。该项目的成果将有助于发展闭环脑刺激的转化医学应用，以及下一代计算神经科学家的教育。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Information Geometry of Orthogonal Initializations and Training

• 发表时间: 2018-10
• 期刊: ArXiv
• 作者: Piotr A. Sokól;Il-Su Park

On 1/n neural representation and robustness

• 发表时间: 2020-12
• 期刊: ArXiv
• 作者: Josue Nassar;Piotr A. Sokól;SueYeon Chung;K. Harris;Il Memming Park

Real-time variational method for learning neural trajectory and its dynamics

学习神经轨迹及其动力学的实时变分方法

• 发表时间: 2023
• 期刊: The Eleventh International Conference on Learning Representations
• 作者: Dowling, Matthew;Zhao, Yuan;Park, Il Memming
• 通讯作者: Park, Il Memming

Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains

• DOI: 10.48550/arxiv.2306.01802
• 发表时间: 2023-06
• 作者: Matthew Dowling;Yuan Zhao;Il Memming Park