An experimental platform to investigate the neural mechanisms underlying flexible decision-making

项目摘要 我们根据上下文灵活选择相关信息以形成决策的能力是一个基本要素， 认知过程，但其潜在的神经机制仍然是未知的。我们的大部分知识， 单神经元分辨力来自于最近在猕猴身上进行的一些研究，在猕猴身上训练动物 以执行需要上下文相关选择和感觉信息整合的任务。在这些任务中， 特征选择是在感官模态内进行的，证据的强度可以精确地 由实验者滴定。尽管这些研究具有开创性的影响，但到目前为止，只有少数大脑 区域进行了研究，还没有进行扰动实验。为了解决这些问题， 我们将训练大鼠进行情境依赖性选择和感官信息整合， 利用啮齿类动物中可用的强大实验工具来解剖负责这一过程的神经回路 行为首先，我们将开发一个新的基于脉冲的任务，需要上下文相关的选择和集成 感官证据在我们的任务中，老鼠将被呈现一系列随机定时的听觉脉冲，其中每个脉冲都是随机的。 脉冲在其位置（右或左）和其音调频率（高或低）上变化。在不同的试验组中，大鼠将 被提示报告脉冲的普遍位置或其普遍频率。因为在这个任务中 信息将以高度随机但精确已知的脉冲形式呈现给受试者， 成千上万的试验，我们将有机会获得一个高度多样化的刺激样本，使我们能够充分 描述所涉及过程的时间动态。第二，我们将建立一个自动化、高... 吞吐量训练流水线，以有效地训练大鼠执行任务。第三，我们将建立一个互补的 自动化高通量设置，用于收集行为期间的电生理学和光遗传学数据。 第四，我们将开发一系列新颖的建模和统计分析，以利用这一丰富的数据集来探索 底层的计算机制拟议的方法旨在提供一个前所未有的平台 探索灵活决策背后的神经机制。

Project Summary Our ability to flexibly select, based on context, the relevant information to form decisions is a fundamental cognitive process, yet its underlying neural mechanisms are still largely unknown. Most of our knowledge at single-neuron resolution is derived from a few recent studies in macaque monkeys, where animals were trained to perform tasks requiring context-dependent selection and integration of sensory information. In these tasks, feature selection is performed within a sensory modality, and the strength of the evidence can be precisely titrated by the experimenter. Despite the groundbreaking impact of these studies, so far only a handful of brain areas have been studied, and no perturbation experiments have yet been performed. To address these issues, we will train rats to perform context-dependent selection and integration of sensory information, and we will leverage the powerful experimental tools available in rodents to dissect the neural circuits responsible for this behavior. First, we will develop a new pulse-based task requiring context-dependent selection and integration of sensory evidence. In our task, rats will be presented with a train of randomly-timed auditory pulses, where each pulse varies in its location (right or left) and its tone frequency (high or low). In separate blocks of trials, rats will be cued to report either the prevalent location of the pulses, or their prevalent frequency. Because in this task information will be presented to subjects in highly random but precisely known pulses, over the course of hundreds of thousands of trials we will have access to a highly varied sample of stimuli allowing us to fully characterize temporal dynamics of the processes involved. Second, we will develop an automated, high- throughput training pipeline to efficiently train rats to perform the task. Third, we will develop a complementary automated high-throughput setup for the collection of electrophysiology and optogenetics data during behavior. Fourth, we will develop a series of novel modeling and statistical analyses to leverage this rich dataset to probe the underlying computational mechanisms. The proposed approach aims to provide an unprecedented platform to probe the neural mechanisms underlying flexible decision-making.