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

学习作为了解内部状态如何影响决策的窗口

• 批准号: 10462000
• 负责人: ANNE KATHRYN CHURCHLAND
• 金额: $ 58.93万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10462000
• 关键词: Address; Affect; Animals; Area; Behavior; Behavioral; Brain; Brain region; Communication; Cues; Data; Data Analyses; Data Science Core; Decision Making; Dimensions; Disease; Environment; Equilibrium; Future; Genetic; Goals; Hand; Harvest; Histology; Image; Individual; Infrastructure; International; Investments; Laboratories; Lead; Learning; Left; Location; Mathematics; Measurement; Measures; Memory; Modeling; Molecular; Movement; Mus; Neurons; Pattern; Perception; Population; Probability; Property; Reporting; Research; Resolution; Resources; Rewards; Sensory; Shapes; Specific qualifier value; Standardization; Stimulus; Structure; Surveys; Synapses; Techniques; Testing; Time; Training; Update; Visual; Weight; Work; association cortex; autism spectrum disorder; behavioral impairment; behavioral study; expectation; experimental study; flexibility; genetic makeup; genetic manipulation; insight; large datasets; mouse model; neural circuit; neural correlate; neural patterning; neuromechanism; relating to nervous system; response; statistics; tool

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.

摘要/摘要，项目 4 该提案的总体目标是了解内部状态如何影响决策并确定 该项目的目标是概念化内部状态的变化 解释了为什么专家在相同情况下的反应与新手不同，尽管学习是众所周知的。 在不同的大脑结构中产生持久的变化，但关于人口水平仍然存在许多问题 该项目将解决单个区域内的变化以及区域间沟通的变化。 通过研究两个时间尺度的学习来回答问题：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 我们将利用已经开发的标准化行为任务。 在这项任务中，动物判断视觉光栅的空间位置并报告。 首先，我们将研究动物学习此任务的基本版本时的行为和神经活动。 其中左右选择以相同的概率获得奖励，并将告知该数据的解释。 通过核心 D 的行为分析，将描述逐项试验参与与脱离的内部 状态，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。 有了这些工具，我们将使用宽场成像广泛地调查大脑皮层。 当动物从新手过渡到专家状态时，我们将使用这个大型数据集来进行许多培训课程。 学习过程中发生最大变化的区域，并将其作为同时神经群体的目标 使用 Neuropixels 探针进行记录这些测量将使我们能够确定通信方式。 各区域之间皮质和皮质下结构的变化以及这些变化如何与个体学习相平行 该分析将遵循项目 1 中的类似方法：我们将测量幅度和方向。 定义了哪些单次试验波动在区域之间进行通信的通信子空间。 改变无提示任务块上的刺激统计数据，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，这些模型具有不同的遗传和分子异常，这些异常会影响 行为灵活性，特别是适应左右刺激的试验块的能力 我们将确定其中哪些神经活动模式是常见的。 模型与对照组不同，从而识别动物调整决策所需的大脑状态 总而言之，这些实验将测试提案的整体效果。 假设，每个内部状态变化都与特定的神经活动模式相关，并且 神经结构内部和神经结构之间的通信，在多个时间尺度上，从几分钟到几周。 通过这种方式，我们期望提取内部状态如何通过网络控制信息流的一般原则。 大脑，并最终决定下一步该做什么。