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

研究灵活决策神经机制的实验平台

基本信息

批准号：
10366077
负责人：
Carlos D Brody
金额：
$ 24.3万
依托单位：
PRINCETON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-03-05 至 2023-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10366077
关键词：
Address Aging Animal Model Animals Anterior Area Auditory Behavior Behavioral Brain Brain region Cognition Collection Computer Models Computer software Corpus striatum structure Cues Data Data Collection Data Set Decision Making Dementia Diagnosis Electrophysiology (science)Frequencies Goals Hearing Human Impaired cognition Instruction Knowledge Left Location Macaca Methods Mission Modality Modeling Monkeys Names National Institute of Mental Health Nature Neurons Parietal Physiologic pulse Population Population Dynamics Primates Process Public Health Randomized Rattus Recurrence Reporting Research Resolution Rodent Sampling Schizophrenia Science Sensory Series Speed Statistical Data Interpretation Statistical Methods Stimulus Structure Study models System Techniques Time Training Work base burden of illness cognitive process executive function experimental study feature selection flexibility frontal lobe neural circuit neuromechanism novel open source optogenetics relating to nervous system sensory integration sound superior colliculus Corpora quadrigemina tool wireless

项目摘要

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.

项目摘要我们基于上下文灵活选择以做出决定的相关信息的能力是一个基本认知过程，但其潜在的神经机制仍然很大程度上是未知的。我们的大多数知识单神经元分辨率来自猕猴的一些最近研究，在那里训练了动物执行需要与上下文相关的选择和集成感官信息的任务。在这些任务中，在感官方式中进行特征选择，并且证据的强度可以精确由实验者滴定。尽管这些研究产生了突破性的影响，但到目前为止只有少数大脑已经研究了区域，尚未进行扰动实验。为了解决这些问题，我们将训练老鼠执行上下文依赖的感觉信息的选择和集成，我们将利用啮齿动物中可用的强大实验工具来剖析负责此的神经电路行为。首先，我们将开发一个基于脉冲的新任务，需要与上下文相关的选择和集成感官证据。在我们的任务中，将向大鼠出现一系列随机时机的听觉脉冲，每个脉冲脉冲在其位置（右或左）及其音调频率（高或低）变化。在单独的试验中，大鼠将提示以报告脉冲的普遍位置或其流行频率。因为在这个任务中信息将以高度随机但精确的脉冲向受试者提供成千上万的试验我们将可以访问多种多样的刺激样本，使我们能够完全表征所涉及过程的时间动态。其次，我们将开发自动化的高级吞吐量训练管道有效地训练大鼠执行任务。第三，我们将发展一个补充在行为过程中收集电生理学和光遗传学数据的自动高通量设置。第四，我们将开发一系列新颖的建模和统计分析，以利用该丰富的数据集进行探测基础计算机制。拟议的方法旨在提供空前的平台探测灵活决策的基础神经机制。