Behavioral Analysis and Modeling Core

摘要/摘要，核心D：行为分析和建模 该提案的首要目标是了解内部状态如何影响决策，并确定 潜在的神经机制行为分析和建模核心的开发、测试和 应用统计工具严格描述行为状态对于实现这一目标至关重要。这 合作将研究定义在三个不同时间尺度上的行为状态变化： 自发地与参与和脱离任务，那些由不断变化的期望所产生的 在任务期间，以及那些在几天内和几天内学习的结果。这个核心的目标是发展 并扩展新的开源分析工具，从行为和视频数据中提取状态信息 在这三个时间尺度上。首先，研究人员将确定控制选择行为的潜在状态， 在一个实验会话中，使用基于广义隐马尔可夫模型的工具， 线性模型输出。此外，他们还将开发一个模型的分层扩展， 利用拟议的实验项目产生的大量行为数据集。接下来，他们会推断 使用尖端的视频分析方法，在一次试验中观察不同的行为状态。特别是，他们将 应用最先进的无标记跟踪方法来提取动物特征（爪子，舌头，鼻子， 等等）。从行为视频，并扩展这些方法，以获得估计的动物姿态在三维空间 （融合多个相机视图）。然后，它们将联合收割机将无标记跟踪输出与非线性 自动编码器压缩方法，以获得更多信息的半监督，低维数据 视频数据的表示。使用机器学习方法，他们将在时间上分割结果 表示以获得每个试验内的可解释的行为状态（例如，“休息”，“新郎”，“达到”），适合 进行进一步的下游分析。最后，他们将开发新的工具来跟踪行为的动态， 学习的过程。决策策略在培训过程中不断演变，包括培训期间和培训期间， 即使在训练有素的动物中也会有差异。为了描述这些状态变化，研究人员将开发 并应用结合了联合收割机状态空间建模和强化学习方法的新型统计模型， 分析个体动物在接受国际大脑训练时观察到的学习曲线 实验室决策任务。由此产生的框架将量化有多少明显的差异 在动物的学习曲线中，可以归因于识别的学习规则的差异，并将有助于识别 全脑记录中推断学习动力学的神经相关性。所有开发的软件工具将 完全开源，并将通过公共的并行云实现共享，以实现最大的可扩展性 和再现性。

Summary/Abstract, Core D: Behavioral Analysis and Modeling This proposal’s overarching goal is to understand how internal states influence decisions and to identify the underlying neural mechanisms. The Behavioral Analysis and Modeling Core’s development, testing, and application of statistical tools to rigorously characterize behavioral states is critical to achieving this goal. This collaboration will study behavioral state changes defined on three different time scales: those arising spontaneously with engagement and disengagement in a task, those resulting from changing expectations during the task, and those resulting from learning within and across days. The goals of this core are to develop and extend novel open-source analytical tools for extracting state information from behavioral and video data over these three timescales. First, the investigators will identify latent states governing choice behavior, which vary across trials within an experimental session, using tools based on a hidden Markov model with generalized linear model outputs. In addition, they will develop a hierarchical extension of the model to take statistical advantage of the vast behavioral dataset produced by the proposed experimental projects. Next, they will infer behavioral states that vary within a single trial using cutting-edge video analysis methods. In particular, they will apply state-of-the-art markerless tracking methods to extract the position of animal features (paws, tongue, nose, etc.) from behavioral video, and extend these methods to obtain estimates of animal pose in three dimensions (fusing multiple camera views). They will then combine the markerless tracking output with nonlinear autoencoder compression methods to obtain a more informative semi-supervised, low-dimensional data representation of the video data. Using machine learning methods, they will temporally segment the resulting representation to obtain interpretable behavioral states within each trial (e.g., “rest,” “groom,” “reach”), suitable for further downstream analyses. Finally, they will develop new tools to track the dynamics of behavior over the course of learning. Decision-making strategies evolve during training, both within and across sessions, and continue to vary even in well-trained animals. To characterize these state changes, the investigators will develop and apply novel statistical models that combine state-space modeling and reinforcement learning approaches to analyze the learning curves observed in individual animals as they are trained to perform the International Brain Laboratory decision-making task. The resulting framework will quantify how much of the pronounced differences in learning curves across animals can be attributed to differences in identified learning rules, and will help identify neural correlates of inferred learning dynamics in brainwide recordings. All software tools that are developed will be fully open source and will be shared via a public, parallelized cloud implementation for maximal scalability and reproducibility.