Theoretical and Physiological Basis of Priority Maps in the Frontal Eye Field

额叶眼场优先级图的理论和生理基础

• 批准号: 10578673
• 负责人: Anthony James Alers
• 金额: $ 4.01万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10578673
• 关键词: Affect; Algorithms; Association Learning; Basal Ganglia; Behavior; Behavioral; Brain; Characteristics; Cognitive; Complex; Computer Simulation; Cues; Data; Disease; Eye; Eye Movements; Goals; Knowledge; Learning; Maps; Measurement; Measures; Modeling; Monkeys; Motor; Movement; Neurons; Outcome; Physiological; Probability; Property; Recording of previous events; Rewards; Saccades; Schizophrenia; Sensory; Services; Shapes; Signal Transduction; Source; Stimulus; Testing; Time; Vision; Visual; Visual Fields; Visual System; Work; autism spectrum disorder; design; expectation; experience; experimental study; flexibility; fovea centralis; frontal eye fields; improved; in silico; in vivo; interest; neurophysiology; novel; oculomotor behavior; predictive modeling; receptive field; response; simulation; skills; superior colliculus Corpora quadrigemina; theories; training opportunity; visual motor; visual search; visual stimulus

Project Summary Each movement of the eyes is the outcome of a competition between the stimulus at the fovea, the target of the movement, and other potential targets. The frontal eye field (FEF) is thought to maintain a map of priority for saccadic eye movements, combining information about the salience of stimuli with their behavioral relevance to guide the flow of eye movements. The objective of this work is to understand how priority maps form in FEF. The overall hypothesis is that FEF neurons form these maps by learning to anticipate saccades and as a result, integrate a wide range of sensory, motor, and cognitive signals into a singular representation that reflects expectations about the timing and probability of saccades. Recently, we developed a novel simulation of associative learning during natural oculomotor behavior that permits measurement of the visuomotor properties of neurons that arise from a given learning goal. The first aim is to use this simulation to determine if FEF neurons in silico develop visuomotor properties like FEF neurons in vivo when they learn to anticipate saccades. We will simulate several FEF networks, each designed to learn distinct goals related to oculomotor behavior and characterize the properties that develop in each. Our preliminary data suggests that when neurons anticipate movement goals, their visuomotor properties capture many important characteristics of FEF neurons. Specifically, the modeled neurons develop dual visual- and movement-related responses, their visual sensitivity shifts across space around the time of saccades, and they respond more vigorously when visual stimulus in their receptive field is the target of a saccade. Furthermore, both the visual and movement responses are relatively early, consistent with a short-latency subpopulation of FEF neurons. The second aim is to assess a prediction of the model, that expectations about saccades are encoded in FEF visual responses. To do this, we will record single-neuron activity in the FEF of monkeys while they complete blocks of a delayed saccade Go/NoGo task. The model predicts that manipulations of the probability or time at which a saccade follows a visual stimulus will modify subsequent visual responses. The third aim is to determine how reward affects the visual sensitivity of FEF neurons. We will alter the reward contingences in the Go/NoGo task to test if FEF neurons encode reward- related information or if their apparent sensitivity to reward is at the service of encoding movement-related information. The outcomes of the second and third aims will be applied to improve the model as needed and generate new predictions. Collectively, this work will establish how the visual responses of FEF neurons are shaped by experience about saccades and reward and provide a rigorous basis for understanding the formation of priority maps in FEF. This basic knowledge is a prerequisite to identifying the source of deficits in saccadic behavior in disorders like schizophrenia and autism. Through training opportunities during this project, I will develop the skills necessary to transition from computational to experimental research.

项目摘要 眼睛的每一个运动都是动脉凹的刺激之间的竞争结果， 运动和其他潜在目标。人们认为额眼场（FEF）可以维持优先级的地图 结合有关刺激显着性的信息及其行为相关的信息 指导眼动。这项工作的目的是了解优先地图在FEF中的形成方式。这 总体假设是，FEF神经元通过学习预测扫视和结果，形成这些地图。 将各种感觉，运动和认知信号整合到反映的单数表示中 对扫视的时间和概率的期望。最近，我们开发了一个新颖的模拟 自然眼动行为过程中的联想学习允许测量视觉运动特性 由给定的学习目标产生的神经元。第一个目的是使用此模拟来确定FEF神经元是否 在计算机中，当他们学会预测扫视时，在体内发展了视觉运动特性。我们将 模拟多个FEF网络，每个网络旨在学习与动眼行为相关的不同目标和 表征每种属性的属性。我们的初步数据表明，当神经元预期 运动目标，它们的视觉运动特性捕获了FEF神经元的许多重要特征。 具体而言，建模的神经元会产生双重视觉和运动相关的响应，它们的视觉敏感性 在扫视时期，在空间上转移，当视觉刺激中的视觉刺激时，它们会更积极地做出反应 接受场是扫视的目标。此外，视觉和运动响应都是相对的 早期，与FEF神经元的短期亚群一致。第二个目的是评估预测 在模型中，对扫视的期望是在FEF视觉响应中编码的。为此，我们将记录 猴子FEF中的单神经元活动完成了延迟的扫视/nogo任务的块。 该模型预测，扫视遵循视觉刺激的概率或时间的操纵将 修改随后的视觉响应。第三个目的是确定奖励如何影响 FEF神经元。我们将更改go/nogo任务中的奖励偶然性，以测试FEF神经元是否编码奖励 - 相关信息或他们对奖励的明显敏感性是为与运动有关的编码 信息。第二和第三目的的结果将用于根据需要改进模型，并且 生成新的预测。总的来说，这项工作将确定FEF神经元的视觉反应如何 根据有关扫视和奖励的经验来塑造的，并为理解形成提供了严格的基础 FEF中的优先级地图。这种基本知识是识别Saccadic中缺陷来源的先决条件 精神分裂症和自闭症等疾病的行为。通过该项目期间的培训机会，我将 发展从计算到实验研究所需的技能。