Mapping neural ensemble computations to biological circuitry in motor control and decision making - Resubmission - 1

将神经集成计算映射到运动控制和决策中的生物电路 - 重新提交 - 1

• 批准号: 10459591
• 负责人: Matthew Tyler Kaufman
• 金额: $ 39.41万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10459591
• 关键词: Affect; Algorithms; Animals; Area; Behavior; Biological; Biology; Brain; Calcium; Categories; Complex; Computer Analysis; Data; Decision Making; Dimensions; Disease; Electrophysiology (science); Engineering; Exhibits; Foundations; Generations; Genetic; Goals; Head; Image; Individual; Interneurons; Joystick; Knowledge; Left; Link; Machine Learning; Mammals; Maps; Mathematics; Monkeys; Motor; Motor Cortex; Motor output; Movement; Mus; Muscle; Neurons; Neurophysiology - biologic function; Noise; Output; Paralysed; Parietal; Parkinson Disease; Pathway interactions; Patients; Pattern; Population; Process; Recording of previous events; Signal Transduction; Stimulus; Stroke; Synapses; System; Systems Analysis; Techniques; Testing; Time; Tissues; Traumatic Brain Injury; Variant; Visual; Work; brain machine interface; cell type; dynamic system; fighting; flexibility; improved; information processing; insight; mind control; motor control; motor disorder; mouse model; neural circuit; neuromechanism; novel; operation; optogenetics; relating to nervous system; stroke therapy; theories; tool; two-photon; visual motor

Project Summary/Abstract Movement is the primary way in which animals interact with the world. To produce the incredibly adaptable behavior of mammals the brain must continually choose actions, then flexibly generate control signals for the body. The main objective of this project is to understand how ensembles of neurons and brain areas work together to control movement and make simple decisions. To understand how behavior is generated, we must know: How is a high-level decision (e.g., reach left vs. right) transformed into a time-varying command signal? And, how does this transformation and the generation of complex outputs exploit the precise biology of neural tissue to function reliably, despite the inherent noisiness of neurons? This goal is critical not only to better understand how neural tissue implements challenging computations, but also because deeper knowledge of these processes is likely to improve treatments for motor disorders such as Parkinson’s and disorders heavily affecting neural connections such as stroke and traumatic brain injury. To achieve this aim, we pair the power of experimental tools in mice together with dynamical systems analysis, which provides mathematical tools for investigating the function of neural ensembles. In the monkey, the dynamical systems approach has led to many insights. For example, we have learned that activity in motor cortex unfolds over time according to oscillatory dynamical “rules”; that much of motor cortical activity exists to support these dynamical rules and does not influence movement directly; and that there is a separation between signals for what movement will be made and when it will be initiated. In the mouse, we propose taking this approach several steps further by mapping the dynamical rules to specific biological features, such as cortical layers and projection pathways. In Aim 1, we will use two-photon calcium imaging to record neural activity during a simple reaching task that elicits variable movements from the mouse. We will then exploit this variability with our dynamical systems tools to identify the rules that govern the M1 pattern generator, and uncover how these rules map to cortical layers. In Aim 2, we will determine how information processing is divided into stages as signals are passed from visual decision areas to motor areas. This second Aim will employ a more complex visually-guided joystick task, together with optogenetic inhibition of specific pathways, calcium imaging, and retrograde tracing. This will allow us to compare activity in the neurons that connect areas with those that are engaged only in local processing. Finally, in Aim 3, we will record from identified projection neurons and apply powerful new machine learning techniques to test two competing theories of how the brain produces consistent outputs: whether the brain suppresses neural “noise” in the output neurons themselves, or suppresses only task-relevant noise according to a more population- oriented approach. These findings will advance our understanding of sophisticated cortical processes including decision making and motor control by grounding theoretical ideas about neural computation in our biological understanding of the tissue that implements it.

项目总结/摘要 运动是动物与世界互动的主要方式。产生了令人难以置信的适应性行为 哺乳动物的大脑必须不断地选择动作，然后灵活地为身体产生控制信号。主要 这个项目的目标是了解神经元和大脑区域的集合如何共同控制运动 并做出简单的决定。为了理解行为是如何产生的，我们必须知道：一个高层决策是如何产生的（例如， 向左与向右）转换为时变命令信号？这种转变和 复杂输出的产生利用了神经组织的精确生物学来可靠地发挥作用，尽管固有的 神经元的噪声这一目标不仅对于更好地理解神经组织如何实现具有挑战性的 这不仅是因为对这些过程的深入了解可能会改善对运动障碍的治疗， 如帕金森氏症和严重影响神经连接的疾病，如中风和创伤性脑损伤。到 为了实现这一目标，我们将小鼠实验工具的力量与动力系统分析结合起来， 为研究神经系统的功能提供了数学工具。在猴子身上， 方法导致了许多见解。例如，我们已经了解到，运动皮层的活动随着时间的推移而展开， 振荡动力学“规则”;运动皮层活动的存在，以支持这些动力学规则，并没有 直接影响运动;并且将进行什么运动以及何时进行运动的信号之间存在分离， 将被启动。在鼠标中，我们建议通过将动态规则映射到 特定的生物学特征，如皮质层和投射通路。在目标1中，我们将使用双光子钙 成像以记录在简单的达到任务期间的神经活动，该任务从小鼠引出可变的运动。我们将 然后利用我们的动态系统工具来识别管理M1模式生成器的规则， 并揭示这些规则如何映射到皮层。在目标2中，我们将确定如何划分信息处理 当信号从视觉决定区传递到运动区时，这第二个目标将采用一个更复杂的 视觉引导的操纵杆任务，以及特定通路的光遗传学抑制，钙成像和逆行 追踪这将使我们能够比较连接区域的神经元与仅参与局部活动的神经元的活动。 处理.最后，在目标3中，我们将从识别的投射神经元中记录，并应用强大的新机器学习 技术来测试大脑如何产生一致输出的两个相互竞争的理论：大脑是否抑制 输出神经元本身的神经“噪声”，或者根据更多的群体仅抑制任务相关的噪声， 导向的方法。这些发现将促进我们对复杂的皮质过程的理解，包括决策 制造和运动控制的基础理论思想的神经计算在我们的生物学理解， 执行它的组织。