NCS-FO: Connecting Spikes to Cognitive Algorithms

NCS-FO：将尖峰连接到认知算法

• 批准号: 1734910
• 负责人: Il Park
• 金额: $ 71.52万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734910&HistoricalAwards=false
• 关键词: NCS; FO; Connecting; Spikes; Cognitive

Experimental neuroscientists can record the signals communicated among the neurons that are collectively involved in producing meaningful behaviors, but making sense of these patterns of activity in terms of specific mental functions is challenging. This research project aims to discover the unseen mental processes that underlie such meaningful behavior from those recordings. The technology developed in this endeavor will uncover new ways of understanding mental processes hidden deep in the noisy signals collected from multiple neurons and will be used to derive new theoretical models (cognitive algorithms) to explain how populations of neurons work together. Such models will contribute to the development of diagnostic tools and neural prosthetics for cognitive dysfunctions in perception, working memory, and decision making, and can also inspire advances in machine learning and artificial intelligence. The technical goal of this project is to develop a data-driven framework amenable to visualization and interpretation of neural activity underlying cognition. The core of the project is the identification and recovery of an interpretable low-dimensional nonlinear continuous dynamical system that underlies observed neural time series, and its validation through experimental perturbations. This will answer two key scientific questions: (1) How are task and cognitive variables represented in low-dimensional neural trajectories; and (2) What are the laws that govern the time evolution of the neural states. Answering these questions will help us understand how subjects implement and switch between different cognitive strategies, and more importantly, will provide a means for testing previously proposed theoretical models of the neural computations underlying cognition. This project will develop a number of statistical methods that can (i) extract private and shared noise from single-trial electrophysiological observations, (ii) combine recordings from multiple sessions to infer a common cognitive neural dynamics model, and (iii) design control stimulation to perturb the current neural state. Specifically, these tools will be applied to recordings from cortical areas involved in visuomotor decision-making to discover (1) how the co-variability in a population of sensory neurons encodes decision variables, (2) how the cognitive strategy changes when sensory evidence statistics change, and (3) the underlying dynamics that sustain spatial working memory. The success of this project could transform how the field analyzes population activity with low-dimensional structure in the context of cognitive tasks and beyond.

实验神经科学家可以记录神经元之间传递的信号，这些神经元共同参与产生有意义的行为，但根据特定的心理功能来理解这些活动模式是具有挑战性的。该研究项目旨在从这些录音中发现这些有意义的行为背后隐藏的心理过程。这项工作中开发的技术将揭示理解隐藏在从多个神经元收集的噪声信号深处的心理过程的新方法，并将用于推导新的理论模型（认知算法）来解释神经元群体如何协同工作。这些模型将有助于开发针对感知、工作记忆和决策等认知功能障碍的诊断工具和神经修复体，并且还可以激发机器学习和人工智能的进步。该项目的技术目标是开发一个数据驱动的框架，能够可视化和解释认知背后的神经活动。该项目的核心是识别和恢复可解释的低维非线性连续动力系统，该系统是观察到的神经时间序列的基础，并通过实验扰动对其进行验证。这将回答两个关键的科学问题：（1）任务和认知变量如何在低维神经轨迹中表示； （2）控制神经状态时间演化的规律是什么。回答这些问题将帮助我们理解受试者如何实施和在不同的认知策略之间切换，更重要的是，将为测试先前提出的认知神经计算理论模型提供一种手段。该项目将开发多种统计方法，这些方法可以（i）从单次试验的电生理观察中提取私人和共享的噪音，（ii）结合多个会话的记录来推断共同的认知神经动力学模型，以及（iii）设计控制刺激来扰乱当前的神经状态。具体来说，这些工具将应用于涉及视觉运动决策的皮层区域的记录，以发现（1）感觉神经元群体中的协变性如何编码决策变量，（2）当感觉证据统计变化时认知策略如何变化，以及（3）维持空间工作记忆的潜在动态。该项目的成功可能会改变该领域在认知任务及其他背景下分析低维结构人口活动的方式。

项目成果

Stimulus-choice (mis)alignment in primate area MT

灵长类区域 MT 的刺激选择（错误）排列

• DOI: 10.1371/journal.pcbi.1007614
• 发表时间: 2020
• 期刊: PLOS Computational Biology
• 影响因子: 4.3
• 作者: Zhao, Yuan;Yates, Jacob L.;Levi, Aaron J.;Huk, Alexander C.;Park, Il Memming
• 通讯作者: Park, Il Memming

Learning structured neural dynamics from single trial population recording

从单个试验群体记录中学习结构化神经动力学

• 发表时间: 2018
• 期刊: & Computers
• 作者: Nassar, J.;Linderman, S.;Zhao, Y.;Bugallo, M.;Park, I. M.
• 通讯作者: Park, I. M.

Information Geometry of Orthogonal Initializations and Training

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

Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling

用于多尺度建模的树结构循环切换线性动力系统

• 发表时间: 2019
• 期刊: International Conference on Learning Representations (ICLR
• 作者: Nassar, J.;Linderman, S.W.;Bugallo, M.;Park, I.M.
• 通讯作者: Park, I.M.

Adjoint dynamics of stable limit cycle neural networks

稳定极限环神经网络的伴随动力学

• 发表时间: 2019
• 期刊: Systems and Computers
• 作者: Sokol, P.;Jordan, I.;Kadile, E.;Park, I. M.
• 通讯作者: Park, I. M.