Deconstructing functional circuits of motor cortex during motor learning

运动学习过程中运动皮层功能回路的解构

• 批准号: 10521778
• 负责人: Takaki Komiyama
• 金额: $ 53.37万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10521778
• 关键词: Area; Axon; Behavioral; Brain; Brain region; Cells; Collaborations; Data; Data Science; Dissection; Impairment; Individual; Learning; Mediating; Methodology; Modeling; Motor Cortex; Motor output; Movement; Multiple Sclerosis; Mus; Nature; Neurons; Parkinson Disease; Pattern; Process; Property; Recurrence; Resolution; Shapes; Subgroup; Synapses; Techniques; Testing; Thalamic structure; Work; base; motor control; motor disorder; motor learning; neural circuit; neural network; optogenetics; professor; relating to nervous system; response; simulation; tool

Generating adaptive body movements is a fundamental function of the brain. The primary motor cortex (M1) is a central locus for motor learning and execution. M1 receives long-range inputs from several brain areas and processes this information through local recurrent connections. However, how various inputs and local connections work together to shape M1 activity is unknown. In this proposal, we will investigate the contributions of long-range inputs and local connections to M1 activity and how they are shaped during motor learning. We will do this using a well-established motor learning paradigm in mice. Our central hypothesis is that the functional properties of individual M1 neurons are defined by their responses to long-range inputs and local neurons. We further hypothesize that learning induces a reorganization of the responses of M1 neurons to specific inputs. This proposal will investigate the nature of the information that M1 receives from various upstream regions, the unique activity patterns of M1 neurons that receive these inputs, and how the local M1 network contributes to their activity patterns.

产生自适应的身体运动是大脑的基本功能。初级运动皮层（M1） 运动学习和执行的中枢。M1从几个大脑区域接收长距离输入， 通过本地循环连接处理此信息。然而，各种投入和当地 连接共同作用塑造M1活动是未知的。在本提案中，我们将调查 长距离输入和局部连接对M1活动的贡献，以及它们在运动过程中是如何形成的 学习我们将在小鼠中使用完善的运动学习范例来实现这一点。我们的核心假设是 单个M1神经元的功能特性是由它们对长距离输入的反应定义的， 局部神经元我们进一步假设，学习诱导M1神经元的反应重组 具体的投入。该提案将调查M1从各种来源收到的信息的性质。 上游区域，接收这些输入的M1神经元的独特活动模式，以及局部M1神经元如何在这些区域中活动。 网络有助于他们的活动模式。