Contextual modulation of visual decision-making across the visual hierarchy

跨视觉层次结构的视觉决策的上下文调制

基本信息

批准号：
10658176
负责人：
PAMELA REINAGEL
金额：
$ 71.1万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10658176
关键词：
Affect Animal Model Animals Area Arousal Attention BRAIN initiative Behavioral Behavioral trial Brain Collaborations Complex Computer Models Cues Data Data Set Decision Making Dependence Diffusion Discipline Discrimination Dissection Dorsal Electrophysiology (science)Environment Foundations Future Goals Image Individual Interdisciplinary Study Knowledge Learning Link Location Masks Measures Methods Modeling Motion Motivation Movement Mus Pathway interactions Persons Preparation Property Protocols documentation Rattus Recording of previous events Research Research Personnel Role Sampling Sensory Shapes Signal Transduction Source Stimulus Stream Testing Time Time Series Analysis Training Transgenic Organisms Variant Viral Vector Vision Visual Visual System Water Work area striata density design experimental study extrastriate visual cortex food restriction improved innovation insight luminance millisecond neural neural circuit neural correlate neuromechanism novel optogenetics sound statistics superior colliculus Corpora quadrigemina

项目摘要

Project Summary/Abstract In sensory decision-making, choices are influenced by non-sensory factors such as motivation, attention, and recent trial history. We seek to incorporate these influences into a drift diffusion model (DDM), by modeling non-sensory variables as deterministic modulators of the starting point or drift rate of sensory evidence accumulation. However, decision-making models are subject to confounds due to the non-stationarity and correlations in long-term behavioral data. More work is needed to quantify these properties and develop new statistical approaches to overcome them. Existing datasets have proven inadequate, so new datasets must be collected. To gain insight into the neural mechanisms of contextual modulation in decision-making, our goal is to compare non-sensory influences on sensory decision- making across levels of the visual hierarchy and between parallel visual streams. We have evidence there are differences, which could be leveraged to identify where in the brain non-visual information enters into visual decision-making. To establish feasibility for an R01, we need to establish a new collaboration with a statistician, develop methods for training single animals in multiple visual tasks; show that visual tasks differ in their sensitivity to non- sensory modulation; and show that we can obtain the amount of trial data required to fit and compare models within subject. We propose to train individual animals in grating orientation, random-dot motion, object identity, spatial location of luminance or contrast, as well as piloting two new tasks (stochastic drifting grating, spatial location of motion). These visual features are thought to be extracted in different brain areas: in primary visual cortex (V1); in different higher visual areas (HVAs) in ventral or dorsal streams; or in a V1-independent collicular pathway. We will collect long-term data on the interleaved tasks using automated high-throughput in-cage testing, and validate that these data meet statistical requirements for model fitting. Rats are ideal for this study because individual rats can learn multiple visual tasks, and we are able to obtain 105-106 behavioral trials per rat without water or food restriction. Rats are also suitable for viral vector targeting strategies and high-density electrophysiology with optogenetics in freely behaving animals. At the end of this 2-year R34 project, the lab will have assembled a new multi-disciplinary research team poised for dissection of underlying circuit mechanisms, with validated visual tasks, training protocols, statistical approaches, and model-fitting methods. These preparations will support a BRAIN Initiative: Targeted BCP R01 application aimed at dissecting neural representations and circuit mechanisms of contextual modulation of choice.

项目概要/摘要在感官决策中，选择受到非感官因素的影响，例如动机、注意力和最近的试验历史。我们力求将这些影响纳入漂移扩散模型（DDM），通过将非感觉变量建模为确定性调制器感官证据积累的起点或漂移率。然而，决策由于长期的非平稳性和相关性，模型容易受到混淆行为数据。需要做更多的工作来量化这些属性并开发新的统计数据克服它们的方法。现有的数据集已被证明是不够的，因此新的数据集必须收集。深入了解情境调节的神经机制决策，我们的目标是比较非感官对感官决策的影响 - 跨越视觉层次结构的层次以及并行视觉流之间的制作。我们有证据表明存在差异，可以利用这些差异来确定大脑的哪个位置非视觉信息进入视觉决策。为了确定 R01 的可行性，我们需要与统计学家建立新的合作，开发培训方法单一动物执行多项视觉任务；表明视觉任务对非非敏感度的敏感性不同感觉调节；并表明我们可以获得拟合所需的试验数据量比较主题内的模型。我们建议以光栅方向训练个体动物，随机点运动、物体识别、亮度或对比度的空间位置以及引导两个新任务（随机漂移光栅、运动的空间位置）。这些视觉特征是被认为是在不同的大脑区域中提取的：初级视觉皮层（V1）；在不同的更高腹侧或背侧视觉区域（HVA）；或在独立于 V1 的丘脑通路中。我们将使用自动化高通量笼内收集有关交错任务的长期数据测试，并验证这些数据是否满足模型拟合的统计要求。老鼠是非常适合这项研究，因为个体老鼠可以学习多种视觉任务，而且我们能够在不限制水或食物的情况下，对每只大鼠进行 105-106 次行为试验。老鼠也适合病毒载体靶向策略和高密度电生理学与光遗传学自由行为动物。在这个为期 2 年的 R34 项目结束时，实验室将组装一个新的多学科研究团队准备剖析潜在的电路机制，经过验证的视觉任务、训练方案、统计方法和模型拟合方法。这些准备工作将支持 BRAIN 计划：有针对性的 BCP R01 应用旨在剖析选择的上下文调制的神经表征和回路机制。