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: 9524750
• 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/9524750
• 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; Dimensions; Disease; Distributed Systems; Electrical Engineering; Engineering; Esthesia; Feedback; Formulation; Future; Gaussian model; 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; analytical method; awake; base; brain computer interface; calcium indicator; cell type; control theory; cost; experience; experimental study; functional plasticity; high dimensionality; insight; interdisciplinary approach; mathematical analysis; medical schools; motor control; motor impairment; motor learning; neural circuit; novel; operation; optogenetics; sensorimotor system; signal processing; spatiotemporal; 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.

在自然界中行动的生物体面临的一个主要目标是根据以下情况选择适当的行动： 感觉信息和通过以前的经验积累的先验知识。了解如何 神经网络处理信息和控制此类行为都需要在大规模方面取得突破 在行为任务和新分析开发过程中的长期数据收集方法 建模工具将能够捕获此类神经网络的动态和组织。 为了解决感觉运动控制和学习背景下的关键问题，我们提出了一个 多学科方法将协同涉及细胞和细胞的四个小组的专业知识 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 建立一个以经验为基础的系统级模型，解释系统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，并且 为未来的实验提供具体的预测。更具体地说，我们打算进一步推进我们的 两个主要方面的理解。首先，研究参与细胞类型的特定成分 运动命令和感觉运动误差预测。我们假设第 2-3 层神经元 与第 5 层神经元具有不同的作用，并且可能更强烈地参与误差估计而不是 比控制。其次，我们打算研究感觉和运动末端是否以及如何变化 以适应新学到的任务。我们再次期望不同皮质之间的差异 层。 这样的框架不仅可以为特定的研究系统提供新的见解，而且可以 可更广泛地转移以探测复杂生物网络的结构和功能。在 此外，开发的独特分析方法和对生物感觉运动的深刻理解 系统可能对机器人和网络控制等领域以及计算机技术的发展做出不可估量的贡献 脑机接口领域的新假肢方法。 相关性（参见说明）： 这项研究计划的目标预计也将为感觉运动控制提供新颖的见解 作为感觉运动学习期间皮质网络的结构和功能可塑性过程。深的 对感觉运动系统的理解将有助于开发新的疾病治疗方式 损害运动功能，例如帕金森病和亨廷顿病。