Bayesian and Decision Theoretic Tools

核心 B：贝叶斯和决策理论工具 运动序列的产生本质上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。这样的估计永远无法确定。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的顺序。火鸡 近一年来每天都喂食的动物在感恩节被宰杀。许多社区，例如 机器人技术、经济学、数据挖掘和人类行为模型正在汇聚到一个共同的方法上： 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 三个实验实验室。我们将继续提取相关变量（时间尺度、概率） 需要由神经系统代表才能有效地产生序列。这些变量将是 与测量的神经信号相关联，以了解这些变量是如何表示的。 此外，在分析神经元数据时，不确定性是核心。当我们询问神经元如何存储和 回想一下运动序列，我们从不直接测量相关变量，例如 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 将许多测量值（例如 1000 个尖峰）转化为具有较小不确定性（或 窄误差线）。我们将使用最先进的贝叶斯数据分析技术来分析由 其他项目中建议的实验。具体来说，我们有兴趣询问神经元如何与 彼此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效果如何 各种类型的信息由测量信号编码。这对于实验项目很有用，因为它 允许询问有关感兴趣变量的多少信息是由神经信号编码的。 相关性（参见说明）： 拟议的工作对于理解实践导致的机制问题至关重要 面对衰老、神经退行性变、中风或脑损伤时人体运动系统的重组。 了解这些机制对旨在保护功能的疗法的设计有影响， 发展补偿器运动并最终发展新颖的运动能力。

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.