Collaborative Research: NCS-FO: Discovering Dynamics in Massive-Scale Neural Datasets Using Machine Learning

几十年来，神经科学家从单个脑细胞（神经元）开始记录，以了解大脑如何感知、做出决定和控制运动。我们现在可以同时记录数百个神经元，但仍处于开发工具的早期阶段，以确定神经元网络如何协同工作来感知世界，并产生产生协调运动所需的控制信号。该项目专注于运动，在理解神经活动的问题上运用了深度学习的力量——强大的新机器学习算法。因为深度学习是在大数据的基础上发展起来的，所以研究人员可以利用大规模的大脑记录。这些记录包括在猴子进行日常活动时记录100个神经元长达一个月的活动，或者在老鼠体内记录数千个神经元长达数小时的活动，每个神经元在大脑中都有一个确切的位置，并与老鼠的持续行为联系在一起。这些方法将打开一扇新的窗口，让我们了解神经元是如何每时每刻共同行动以产生运动的。研究人员将对潜在过程进行简单描述，并通过在线教程、将由埃默里大学和佐治亚理工学院开发的新开放课程、亚特兰大科学节和亚特兰大大脑意识月等活动与公众分享。他们还将公开其数据集，并在重要的科学会议上主持数据教程和建模竞赛，通过更广泛的科学界参与来加速进展。自Ed Evarts首次记录猴子M1中的单个神经元以来的50年里，人们一直致力于理解这些个体信号与过度训练的猴子在高度受限的运动行为中收集的运动相关信号之间的关系。与此同时，理论家们假设，在大脑中进行的计算主要依赖于网络层面的现象：大脑回路中的动态规律限制了活动，并决定了它如何随着时间的推移而演变。该项目的目标是开发一套强大的新工具，基于深度学习，在前所未有的时间和空间尺度上分析这些动态。研究人员将利用猴子自然行为期间长达一个月的M1电生理、肌电图和行为数据记录，以及行为小鼠双光子成像记录的大量神经元。使用顺序自编码器的新型机器学习技术将使研究人员能够了解这些数据背后的动态。这种结合将为大脑对运动行为的控制提供前所未有的窗口。本文开发的新的分析框架将从运动行为扩展到更高层次的错误处理、决策和学习问题。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。

For decades, neuroscientists have recorded from single brain cells (neurons) to understand how the brain senses, makes decisions, and controls movements. We can now record from hundreds of neurons simultaneously but are still at an early stage in developing tools for determining how networks of neurons work together to perceive the world and to generate the control signals needed to produce coordinated movement. Focusing on movement, this project brings to bear the power of deep learning --- powerful new machine learning algorithms --- on the problem of understanding neural activity. Because deep learning thrives on big data, the investigators can leverage massive-scale brain recordings. These include month-long recordings chronicling the activity of 100 neurons as a monkey goes about its daily business, or recording from thousands of neurons for hours in the mouse, each identified with an exact location in the brain and tied to the mouse's on-going behaviors. These approaches will open new windows on how neurons act together moment-by-moment to produce movement. The investigators will develop simple descriptions of the underlying processes to be shared with the public through venues including online tutorials, a new open course that will be developed at Emory University and Georgia Tech, the Atlanta Science Festival, and Atlanta's Brain Awareness Month. They will also make their data sets publicly available, and host data tutorial and modeling competitions at key scientific meetings, to accelerate progress by engaging the broader scientific community.In the fifty years since Ed Evarts first recorded single neurons in M1 of behaving monkeys, great effort has been devoted to understanding the relation between these individual signals and movement-related signals collected during highly constrained motor behaviors performed by over-trained monkeys. In parallel, theoreticians posited that the computations performed in the brain depend critically on network-level phenomena: dynamical laws in brain circuits that constrain the activity and dictate how it evolves over time. The goal of this project is to develop a powerful new suite of tools, based on deep learning, to analyze these dynamics at unprecedented temporal and spatial scales. The investigators will leverage recordings with month-long M1 electrophysiology, EMG, and behavioral data during natural behaviors from monkeys, and vast numbers of neurons recorded with two-photon imaging from behaving mice. Novel machine learning techniques using sequential auto-encoders will enable the investigators to learn the dynamics underlying these data. This combination will provide windows into the brain's control of motor behavior that have never before been possible. The novel analytical framework developed here will be extensible from motor behaviors to higher level problems of error processing, decision making, and learning.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.