Bayesian and Decision Theoretic Tools

核心B：贝叶斯和决策理论工具 动作序列的产生天生就受到不确定性的影响：为了快速移动，动物需要 根据过去的知识估计下一步要做什么。这样的估计永远不可能确定。一个丰富多彩的例子 最近流行的一本书(Taleb，2008)表明，我们永远不能确定一系列事件。火鸡 近一年来每天喂食的鱼在感恩节被宰杀。许多社区，如 机器人学、经济学、数据挖掘和人类行为模型正汇聚在一个共同的方法上，以实现 不确定性的形式化：贝叶斯决策理论。我们将首先使用这些方法来预测每个 三个实验实验室中。我们将继续提取相关变量(时间尺度、概率) 需要由神经系统来代表才能有效地产生序列。然后这些变量将是 与测量的神经信号相关联，以询问这些变量是如何表示的。 此外，在分析来自神经元的数据时，不确定性是核心问题。当我们问神经元是如何存储和 回忆运动序列我们从不直接测量相关变量，例如记忆，我们测量的是尖峰 或受噪声影响的成像信号。因此，神经数据分析的一个中心主题是将 许多测量值(例如1000个峰值)进入具有小不确定性(或 窄误差条)。我们将使用最先进的贝叶斯数据分析技术来分析 建议在其他项目中进行的实验。具体地说，我们感兴趣的是神经元是如何与 使用这些贝叶斯方法。最后，我们将使用最先进的解码方法来询问 通过测量信号对各种类型的信息进行编码。这对实验项目是有用的，因为它 允许询问神经信号编码了多少有关感兴趣变量的信息。 相关性(请参阅说明)： 拟议的工作是理解实践导致的机制的核心问题 面对衰老、神经退化、中风或脑损伤时人类运动系统的重组。 了解这些机制对旨在保护功能的疗法的设计有影响， 开发补偿器运动，最终开发新的电机容量。

Core B: Bayesian and decision theoretic tools The production of movement sequences is inherently affected by uncertainty: to move rapidly the animal needs to estimate what to do next given past knowledge. Such estimates can never be certain. A colorful example from a recently popular book (Taleb, 2008) shows that we can never be certain about a sequence of events. The turkey that has been fed every day for close to a year gets slaughtered for Thanksgiving. Many communities such as robotics, economics, data mining and models of human behavior are converging on a common approach towards formalizing uncertainty: Bayesian decision theory. We will first use these methods to predict behaviors from each of the three experimental labs. We will continue to extract the relevant variables (timescales, probabilities) that need to be represented by the nervous system to efficiently produce sequences. These variables will then be correlated with measured neural signals to ask how these variables are represented. Moreover, uncertainty is central when analyzing data from neurons. When we are asking how neurons store and recall motor sequences we never directly measure the relevant variables, such as niemory, we rather measure spikes or imaging signals that are affected by noise. A central topic for neural data analysis, therefore, is to combine many measurements (say 1000 spikes) into an estimate (of say tuning properties) that has small uncertainty (or narrow error-bars). We will use state of the art Bayesian data analysis techniques to analyze the data resulting from the proposed experiments in the other projects. Specifically we are interested in asking how neurons interact with one another using these Bayesian methods. Lastly, we will use state of the art decoding methods to ask how well various types of information are encoded by the measured signals. This is useful for the experimental projects as it allows asking how much information about a, variable of interest is encoded by neural signals. RELEVANCE (See instructions): The proposed work is central to the problem of understanding the mechansims where practice leads to to reorganizafion of the human motor system in the face of aging, neurodenerafion, stroke or brain injury. Understanding these mechansims has an impact on the design of therapies directed at preserving function, developing compensator movements and ulfimately, developing novel motor capacity.