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地选择相关信息以形成决策的能力是基本的 认知过程，但其潜在的神经机制在很大程度上仍不清楚。我们的大部分知识都在 单神经元分辨是从最近对猕猴进行的几项研究中得出的，在这些研究中，猕猴接受了训练 以执行需要上下文相关的选择和整合感觉信息的任务。在这些任务中， 特征选择是在感觉通道内进行的，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，只有少数几个大脑 已经对这些区域进行了研究，但尚未进行任何微扰实验。为了解决这些问题， 我们将训练大鼠进行上下文相关的选择和感觉信息的整合，我们将 利用啮齿动物身上可用的强大实验工具来解剖导致这一现象的神经回路 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 脉冲的位置(右或左)和音调频率(高或低)各不相同。在单独的试验中，大鼠将 被提示以报告脉冲的普遍位置或其普遍频率。因为在这项任务中 信息将在以下过程中以高度随机但精确已知的脉冲呈现给受试者 数十万次试验，我们将有机会获得高度多样化的刺激样本，使我们能够充分 描述所涉及的过程的时间动态。第二，我们将开发一种自动化、高效率的 吞吐量训练流水线，以有效地训练老鼠执行任务。第三，我们将发展互补的 自动高通量设置，用于在行为过程中收集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 潜在的计算机制。拟议的方法旨在提供一个前所未有的平台 探讨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.