CRCNS: Sensory-Motor Integration in Mammalian Brian: experiment, analysis, modeling

CRCNS：哺乳动物布莱恩的感觉运动整合：实验、分析、建模

• 批准号: 9242184
• 负责人: RONALD R COIFMAN
• 金额: $ 20.53万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9242184
• 关键词: Address; Animal Behavior; Animals; Area; Behavior; Behavioral; Biological; Biological Neural Networks; Brain; Chronic; Communication; Complex; Data; Data Analyses; Data Collection; Data Set; Development; Disease; Distributed Systems; Electrical Engineering; Engineering; Esthesia; Feedback; Formulation; Future; Goals; Head; Huntington Disease; Instruction; Israel; Knowledge; Language; Lead; Learning; Literature; Machine Learning; Mathematics; Methodology; Methods; Microscopy; Modality; Modeling; Motor; Motor Activity; Motor Cortex; Movement; Mus; Nature; Neurons; Neurosciences; Noise; Organism; Parkinson Disease; Pharmacogenetics; Physiology; Population; Process; Prosthesis; Psychophysics; Research Proposals; Resolution; Robotics; Role; Sensory; Structural Models; Structure; System; Uncertainty; Universities; Viral; Work; awake; base; brain computer interface; calcium indicator; cell type; control theory; cost; experience; functional plasticity; information processing; insight; interdisciplinary approach; mathematical analysis; medical schools; motor control; motor learning; neural circuit; novel; operation; optogenetics; research study; sensorimotor system; signal processing; success; theories; tool; two-photon

A major goal facing organisms acting in the natural world is the selection of appropriate actions based on sensory information and prior knowledge accumulated through previous experience. Understanding how neural networks process information and control such actions requires a breakthrough both in large scale chronic data collection methods during behavioral tasks and in the development of new analysis and modeling tools that will be able to capture the dynamics and organization of such neural networks. To address key questions in the context of sensory-motor control and learning we propose a multidisciplinary approach that will synergize the expertise of the four groups involved in cellular and systems neuroscience, machine learning, signal processing, control theory and modeling. Our goal is to establish an empirically-grounded systems-level model explaining the interaction and integration within the sensory-motor system during behavioral tasks, which is consistent with the experimental data, and which provides concrete predictions for future experiments. More specifically, we intend to further our understanding on two main fronts. First, study cell type specific components that participate in the movement command and in sensory-motor error prediction. We hypothesize that layer 2-3 neurons subserve different roles from layer 5 neurons, and may be more strongly involved in error estimation rather than in control. Second, we intend to investigate whether and how the sensory and motor ends change in order to adapt to the new learned task. Here again we expect differences between the different cortical layers. Such a framework will not only provide new insights into the specific investigated system, but could be transferrable more generally to probe the structure and functionality of complex biological networks. In addition, the unique analysis methods developed and the deep understanding of biological sensory-motor systems may contribute invaluably to fields such as robotics and network control, and to the development of new prosthetics approaches within the field of Brain Computer Interfaces. RELEVANCE (See instructions): The goals of this research proposal are expected to provide novel insight on sensory-motor control as well as structural and functional plasticity processes of the cortical network during sensory-motor learning. Deep understanding of sensory-motor systems will aid in development of new treatment modalities for diseases that impair motor function, such as Parkinson's and Huntington's diseases.

在自然世界中作用的生物面临的主要目标是根据基于 感官信息和通过以前的经验积累的知识。了解如何 神经网络处理信息和控制此类动作需要大规模突破 行为任务期间的慢性数据收集方法以及新分析的发展和 建模工具将能够捕获此类神经网络的动态和组织。 为了在感觉运动控制和学习的背景下解决关键问题，我们提出了一个 多学科方法，将协同涉及细胞和涉及的四个群体的专业知识 系统神经科学，机器学习，信号处理，控制理论和建模。我们的目标是 建立一个凭经验实现的系统级模型，以解释在内部的相互作用和集成 行为任务期间的感觉运动系统与实验数据一致，哪些是一致的 为将来的实验提供具体的预测。更具体地说，我们打算进一步 在两个主要方面的理解。首先，研究细胞类型的特定组件参与 运动命令和感官运动错误预测。我们假设该第2-3层神经元 从第5层神经元中处理不同的角色，并且可能更强烈地参与误差估计而不是 而不是控制。其次，我们打算调查感觉和电动机的结尾是否发生变化 为了适应新的学习任务。在这里再次，我们期望不同皮质之间的差异 层。 这样的框架不仅可以为特定调查的系统提供新的见解，而且可以是 更普遍的转移可探测复杂生物网络的结构和功能。在 此外，开发了独特的分析方法以及对生物感觉运动的深刻理解 系统可能会导致机器人技术和网络控制等领域以及开发 大脑计算机接口领域内的新假肢方法。 相关性（请参阅说明）： 这项研究建议的目标也有望提供有关感觉运动控制的新见解 作为感觉运动过程中皮质网络的结构和功能可塑性过程。深的 了解感觉运动系统将有助于开发疾病的新治疗方式 这种损害运动功能，例如帕金森氏症和亨廷顿的疾病。