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

摘要/摘要，项目4 这项提案的首要目标是了解内部国家如何影响决策，并确定 潜在的神经机制。这个项目的目标是将内部状态的变化概念化， 解释了为什么在相同的情况下，专家的反应与新手不同。尽管学习是众所周知的 在不同的大脑结构中产生长期的变化，许多关于人口水平的问题仍然存在 单一地区内的变化和地区间交流的变化。该项目将解决这些问题 在两个时间尺度上研究学习的问题：定义动物逐渐掌握的长期学习 感知决策任务，以及动物在获得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 由国际脑实验室提供。在任务中，动物判断视觉栅格的空间位置并报告 这是一种运动。首先，我们将研究动物学习这个任务的基本版本时的行为和神经活动 其中，左选择和右选择以相等的概率得到奖励。对此数据的解释将被告知 根据核心D的行为分析，这些行为分析将表征逐个参与的试验与不参与的内部试验 国家，这种平衡很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 详细的视频分析。有了这些工具，我们将使用广域成像对大脑皮层进行广泛的观察 许多训练课程都像动物一样从新手过渡到专家。我们将使用这个大型数据集来识别 在学习过程中经历最大变化的区域，并将其作为同时神经种群的目标 使用神经像素探头进行录音。这些测量将使我们能够确定如何沟通 大脑皮层和皮质下结构的区域变化以及这些变化如何与个体学习平行 费率。此分析将遵循与项目1类似的方法：我们将测量震级和方向 通信子空间的一部分，该通信子空间定义在区域之间传递哪些单次试验波动。因为我们 改变无提示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，它们具有不同的遗传和分子异常，这些异常会影响 行为灵活性，特别是适应一系列试验的能力，其中左刺激和右刺激 呈现出不相等的概率。我们将确定哪些神经活动模式在这些模式中是常见的 模型和对照不同，从而识别动物需要调整其决定的大脑状态- 随着情况的变化而创造先例。综上所述，这些实验将测试该提案的整体 假设，每个内部状态变化都与特定的神经活动模式相关联，并且 在多个时间尺度上，在神经结构内和跨神经结构进行交流，从几分钟到几周。 通过这种方式，我们希望提取关于内部状态如何管理通过 大脑，并最终决定下一步做什么。

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.