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

在自然界中活动的有机体面临的一个主要目标是根据以下条件选择适当的行动 通过先前的经验积累的感官信息和先验知识。了解如何 神经网络处理信息和控制这类行为需要在大规模两个方面取得突破 在行为任务期间和在新的分析和开发过程中的长期数据收集方法 将能够捕捉此类神经网络的动态和组织的建模工具。 为了解决感觉-运动控制和学习的关键问题，我们提出了一种 多学科方法，将协同四个小组在蜂窝和 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 建立以经验为基础的系统级模型，解释 在行为任务中的感觉-运动系统，这与实验数据一致，并且 为未来的实验提供了具体的预测。更具体地说，我们打算进一步推进我们的 在两个主要方面的理解。首先，研究参与细胞类型的特定组件 运动指令和感觉-运动误差预测。我们假设2-3层神经元 从属于第5层神经元的不同角色，可能更强烈地参与误差估计，而不是 而不是控制。第二，我们打算研究感觉和运动末端是否以及如何改变。 以适应新的学习任务。在这里，我们再次期待不同大脑皮层之间的差异 层次感。 这样的框架不仅将为所调查的具体系统提供新的见解，而且可能是 更普遍地，可用于探测复杂生物网络的结构和功能。在……里面 此外，开发了独特的分析方法，并对生物感觉运动有了深入的了解 系统可能对诸如机器人和网络控制等领域以及对 脑机接口领域的新假肢方法。 相关性(请参阅说明)： 这项研究计划的目标也有望为感觉运动控制提供新的见解。 作为感觉-运动学习过程中大脑皮层网络的结构和功能可塑性过程。深沉 对感觉运动系统的了解将有助于开发新的疾病治疗方式 这会损害运动功能，如帕金森氏症和亨廷顿病。

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.