Learning as a window into how internal states influence decision-making

摘要/摘要，项目4 该提案的首要目标是了解内部状态如何影响决策，并确定 潜在的神经机制这个项目的目标是概念化内部状态的变化， 这解释了为什么专家在相同的情况下会做出与新手不同的反应。尽管众所周知， 在不同的大脑结构中产生持久的变化，许多关于人口水平的问题仍然存在 单个区域内的变化和区域间交流的变化。该项目将解决这些问题 通过在两个时间尺度上研究学习问题：定义动物逐渐掌握的长期学习 知觉决策任务，以及动物在奖励时所经历的会话内学习 统计数据动态变化。我们将利用已经开发的标准化行为任务， 国际脑实验室在任务中，动物判断视觉光栅的空间位置并报告 用一个动作。首先，我们将研究动物学习这项任务的基本版本时的行为和神经活动 左和右选择的概率相等这些数据的解释将被告知 通过核心D的行为分析，该分析将表征参与与脱离的内部试验 这种平衡可能会随着学习而改变。我们还将跟踪学习过程中的行为变化， 详细视频分析有了这些工具，我们将使用宽视野成像技术对大脑皮层进行广泛的调查， 许多训练课程，因为动物从新手过渡到专家状态。我们将使用这个大型数据集来识别 在学习过程中发生最大变化的区域，并将其作为同步神经种群的目标 使用Neuropixels探针记录。这些测量将使我们能够确定通信是如何 皮层和皮层下结构的变化，以及这些变化如何与个体学习平行 rates.该分析将遵循与项目1类似的方法：我们将测量 通信子空间，定义了区域之间通信的单次试验波动。正如我们 改变无线索任务块上的刺激统计，我们还将研究专家的会话内学习。 最后，我们将使用三种自闭症小鼠模型，这些小鼠具有不同的遗传和分子异常， 行为的灵活性，特别是适应试验块的能力，其中左和右刺激是 呈现出不平等的概率。我们将确定哪些神经活动模式是共同的， 模型和不同的控制，从而确定大脑状态，动物需要调整他们的决定- 随着情况的变化而有前科综合起来，这些实验将测试该提案的整体 假设，每个内部状态变化与特定的神经活动模式有关， 神经结构内和跨神经结构的通信，在多个时间尺度上，从几分钟到数周。 通过这种方式，我们希望提取内部状态如何管理信息流的一般原则 大脑，最终决定下一步该做什么。

Summary/Abstract, Project 4 This proposal’s overarching goal is to understand how internal states influence decisions and to identify the underlying neural mechanisms. The goal of this project is to conceptualize the change in internal state that explains why experts respond differently from novices in the same situation. Although learning is well known to produce long-lasting changes in diverse brain structures, many questions remain about the population-level changes within single areas and the changes in communication among areas. This project will address these questions by studying learning on two timescales: the long-term learning that defines animals’ gradual mastery of a perceptual decision-making task, and the within-session learning that animals undergo when reward statistics dynamically change. We will leverage a standardized behavioral task that has already been developed by the International Brain Laboratory. In the task, animals judge the spatial location of a visual grating and report it with a movement. First we will study behavior and neural activity as animals learn the basic version of this task in which left and right choices are rewarded with equal probability. The interpretation of this data will be informed by behavioral analyses from Core D that will characterize trial-by-trial engaged versus disengaged internal states, a balance that will likely change over learning. We will also track behavioral changes over learning using detailed video analysis. With these tools in hand, we will survey the cortex broadly using widefield imaging over many training sessions as animals transition from novice to expert status. We will use this large dataset to identify the areas that undergo the largest changes during learning and target them for simultaneous neural population recordings using Neuropixels probes. These measurements will allow us to determine how communication among areas changes in cortical and subcortical structures and how these changes parallel individual learning rates. This analysis will follow a similar approach from Project 1: we will measure the magnitude and orientation of a communication subspace that defines which single-trial fluctuations are communicated among areas. As we change the stimulus statistics over uncued task blocks, we will also study within-session learning of experts. Finally, we will use three mouse models of autism with distinct genetic and molecular anomalies that affect behavioral flexibility and specifically the ability to adapt to blocks of trials in which left and right stimuli are presented with unequal probability. We will establish which neural activity patterns are common among these models and different from controls and thus identify the brain states that animals require to adjust their decision- making priors as circumstances change. Taken together, these experiments will test the proposal’s overall hypothesis, that each internal state change is associated with a specific pattern of neural activity and communication within and across neural structures, on multiple timescales, from a few minutes to many weeks. In this way, we expect to extract general principles for how internal states govern information flow through the brain and, ultimately, decisions about what to do next.