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: 10389556
• 负责人: Anthony James Alers
• 金额: $ 3.9万
• 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/10389556
• 关键词: Affect; Algorithms; 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; Rewards; Saccades; Schizophrenia; Sensory; Services; Signal Transduction; Source; Stimulus; Testing; Time; Vision; Visual; Visual Fields; Work; autism spectrum disorder; classical conditioning; 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; 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 眼睛的每一次运动都是中央凹处的刺激与眼睛运动的目标之间竞争的结果。 移动和其他潜在目标。前眼区（FEF）被认为是维持优先级的地图， 扫视眼球运动，结合信息的显着性刺激与他们的行为相关性， 引导眼球运动。这项工作的目的是了解优先级地图如何形成FEF。的 总的假设是FEF神经元通过学习预测扫视形成这些图， 将广泛的感觉、运动和认知信号整合成一个单一的表征， 关于扫视的时间和概率的期望。最近，我们开发了一种新的模拟， 在允许测量视觉特性的自然视觉行为期间的联想学习 由特定的学习目标产生的神经元。第一个目的是使用这种模拟来确定FEF神经元是否 在计算机中，当它们学习预测扫视时，它们会像体内的FEF神经元一样发展视觉特性。我们将 模拟几个FEF网络，每个网络都旨在学习与眼动行为相关的不同目标， 描述每一种物质的特性。我们的初步数据表明，当神经元预期 运动目标，它们的视觉特性捕获了FEF神经元的许多重要特征。 具体来说，模型神经元发展双重视觉和运动相关的反应，他们的视觉敏感性， 在扫视的时候，它们会在空间上发生变化，当视觉刺激在它们的大脑中时，它们的反应会更强烈。 感受野是扫视的目标。此外，视觉和运动反应都相对较弱。 早期，与FEF神经元的短潜伏期亚群一致。第二个目的是评估一个预测 的模型，对扫视的期望编码在FEF视觉反应。为此，我们将记录 猴子完成延迟扫视Go/NoGo任务时FEF中的单神经元活动。 该模型预测，操纵扫视跟随视觉刺激的概率或时间将 改变随后的视觉反应。第三个目的是确定奖励如何影响视觉敏感性， FEF神经元。我们将改变Go/NoGo任务中的奖励条件，以测试FEF神经元是否编码奖励- 相关信息，或者他们对奖励的明显敏感性是否为编码运动相关信息服务。 信息.第二和第三个目标的成果将用于根据需要改进模型， 产生新的预测。总的来说，这项工作将建立如何视觉反应的FEF神经元是 由关于扫视和奖励的经验形成，并为理解形成提供了严格的基础。 FEF中的优先级地图。这些基本知识是识别扫视缺陷来源的先决条件。 精神分裂症和自闭症等疾病的行为。通过本项目期间的培训机会，我将 培养从计算到实验研究过渡所需的技能。