Using multiple species, stimuli, and tasks to study the neural basis of visually guided behavior

使用多种物种、刺激和任务来研究视觉引导行为的神经基础

• 批准号: 10040904
• 负责人: Amy Meesun Ni
• 金额: $ 12.01万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10040904
• 关键词: Address; Affect; Algorithms; Attention; Attention deficit hyperactivity disorder; Behavior; Behavioral; Brain; Child; Cognitive; Complex; Computational Technique; Crude Extracts; Cues; Data Analyses; Data Set; Disease; Environment; Etiology; Goals; Grant; Human; Information Retrieval; Instruction; Learning; Light; Manufactured football; Measurement; Measures; Mentors; Monkeys; Neuronal Differentiation; Neurons; Perception; Perceptual learning; Performance; Population; Positioning Attribute; Process; Psychophysics; Research; Research Personnel; Running; Specificity; Stimulus; Techniques; Testing; Time; Training; United States; Visual; Visual Cortex; Visual Perception; Visual system structure; Work; behavior test; biophysical model; career; cognitive process; experimental study; flexibility; high dimensionality; human subject; nervous system disorder; neuromechanism; programs; relating to nervous system; response; skills; virtual; visual stimulus

Project Summary The visual system must constantly extract behaviorally relevant stimulus information from an abundance of irrelevant inputs from the environment, using cognitive phenomena such as attention and learning to guide this continuously adapting process. Understanding the mechanisms by which task-relevant information is extracted from the high-dimensional activity of neuronal populations will be vital to understanding the complex etiology of many neurological diseases, such as disorders of attention. A longstanding assumption has been that this process is optimized for each specific visual task, maximizing the amount of information extracted from the activity of neuronal populations. While this may be possible in highly reductionist lab settings with simple stimuli, such specific optimization would be virtually impossible in the face of the abundant and rapidly changing stimuli and task goals encountered in the natural world. Our recent work suggests a new hypothesis: the extraction of information from neuronal population activity is optimized not for each specific visual task, but generally for the wide variety of stimuli and tasks encountered in realistic environments. In each of our Aims, we will use feature-rich, realistic visual stimuli, precise psychophysical measurements of perceptual performance, simultaneous recordings from populations of visual neurons, and cutting edge data analysis techniques to test one prediction of our central hypothesis. In Aim 1, we will test the prediction that in realistic environments with changes to both task-relevant and -irrelevant visual features, neuronal information extraction is optimized generally for all of the encountered feature changes, instead of just for the task-relevant changes. In Aims 2 and 3, we will test the extent to which our central hypothesis is true across different time frames. In Aim 2, we will test the prediction that the neuronal information extraction process is optimized to be flexible on short time scales, in the face of rapidly changing task goals. In Aim 3, we will test the prediction that information extraction can also be flexibly optimized on long time scales, explaining gradual and highly specific improvements in perceptual ability due to perceptual learning. The results of these studies will have broad implications both for biophysical models of visual perception and for our understanding of how neuronal mechanisms in general are able to flexibly adapt to our constantly changing natural environment. The proposed project will not only further our understanding of how neuronal activity guides behavior in the context of realistic visual environments, but will provide me with the necessary technical and analytical skills to launch my career as an independent investigator. By receiving expert training to create and operate complex, feature-rich visual stimuli, to collect precise psychophysical measurements by parametrically varying specific aspects of those stimuli, and to apply advanced computational techniques to analyze complementary behavioral and neuronal datasets, I will be fully prepared to independently pursue questions of how neuronal activity guides perception and behavior in my own research program.

项目摘要 视觉系统必须不断地从丰富的信息中提取与行为相关的刺激信息。 来自环境的不相关输入，使用注意力和学习等认知现象来指导这一过程。 不断适应过程。理解任务相关信息的提取机制 从神经元群体的高维活动将是至关重要的理解复杂的病因 许多神经系统疾病，如注意力障碍。一个长期存在的假设是， 为每个特定的视觉任务进行优化，最大限度地提高从 神经元群体的活动。虽然这在高度简化的实验室环境中是可能的， 刺激，这种具体的优化将是几乎不可能的，在面对丰富和迅速 在自然界中遇到的不断变化的刺激和任务目标。我们最近的工作提出了一个新的假设： 从神经元群体活动中提取信息并不是针对每个特定的视觉任务进行优化， 通常用于现实环境中遇到的各种各样的刺激和任务。 在我们的每个目标中，我们将使用功能丰富，逼真的视觉刺激，精确的心理物理测量 感知性能，视觉神经元群体的同步记录，以及尖端数据 分析技术来测试我们的中心假设的一个预测。在目标1中，我们将测试预测， 现实的环境与任务相关和不相关的视觉特征，神经元信息的变化 提取通常针对所有遇到的特征变化进行优化，而不仅仅是针对任务相关的 变化在目标2和3中，我们将检验我们的中心假设在不同时间的真实程度。 跳转在目标2中，我们将测试神经元信息提取过程被优化为 在短时间内灵活应对快速变化的任务目标。在目标3中，我们将测试预测， 信息提取也可以在长时间尺度上灵活优化，解释渐进和高度具体的 由于感知学习而导致的感知能力的提高。这些研究的结果将具有广泛的 这对视觉感知的生物物理模型和我们对神经元如何 一般来说，机械装置能够灵活地适应我们不断变化的自然环境。 该项目不仅将进一步加深我们对神经元活动如何指导行为的理解， 现实的视觉环境的背景下，但将为我提供必要的技术和分析 作为一名独立调查员的职业生涯通过接受专家培训， 复杂、特征丰富的视觉刺激，通过参数变化收集精确的心理物理测量结果 这些刺激的具体方面，并应用先进的计算技术来分析互补的 行为和神经元数据集，我将充分准备独立追求的问题，如何神经元 在我自己的研究项目中，活动指导感知和行为。