Bayesian and Decision Theoretic Tools

核心B：贝叶斯和决策理论工具 运动序列的产生固有地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该做什么。这种估计永远无法确定。一个丰富多彩的例子， 最近流行的一本书（Taleb，2008）表明，我们永远无法确定一系列事件。火鸡 在感恩节被宰杀。许多社区，如 机器人技术、经济学、数据挖掘和人类行为模型正在汇聚成一种共同的方法， 形式化不确定性：贝叶斯决策理论。我们将首先使用这些方法来预测每个人的行为， 这三个实验室。我们将继续提取相关变量（时间尺度，概率）， 需要由神经系统来表示，以有效地产生序列。这些变量将被 与测量的神经信号相关，以了解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。当我们问神经元如何储存和 回忆运动序列，我们从来没有直接测量相关变量，如niemory，我们宁愿测量尖峰 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联合收割机 将许多测量（例如1000个尖峰）转换为具有小的不确定性的（例如调谐特性的）估计（或者 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以下过程产生的数据 在其他项目中进行的实验。具体来说，我们感兴趣的是询问神经元如何与 用贝叶斯方法来分析。最后，我们将使用最先进的解码方法来询问 各种类型的信息由所测量的信号编码。这对实验项目很有用，因为它 允许询问神经信号编码了多少关于感兴趣变量的信息。 相关性（参见说明）： 所提出的工作是理解实践导致的机制问题的核心， 在面对衰老、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留功能的治疗设计有影响， 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.