Neural mechanisms of active vision in the fovea

中央凹主动视觉的神经机制

• 批准号: 10641157
• 负责人: Jacob L Yates
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10641157
• 关键词: Address; Age related macular degeneration; American; Behavior; Behavioral; Blindness; Brain; Callithrix; Code; Cognitive; Computer Models; Custom; Data; Data Set; Development; Development Plans; Disease; Ensure; Esthesia; Eye; Eye Movements; Foundations; Frequencies; Funding; Goals; Grant; Human; Image; Laboratories; Machine Learning; Measurement; Measures; Mentors; Modeling; Modernization; Monkeys; Motion; Neurons; Outcome; Peripheral; Phase; Physiology; Population; Positioning Attribute; Primates; Process; Recovery; Research; Resolution; Retina; Retinal Diseases; Role; Saccades; Sampling; Series; Signal Transduction; Statistical Models; Stream; System; Techniques; Testing; Training; Universities; V1 neuron; V4 neuron; Vision; Visual; Visual Acuity; Visual Cortex; Visual Fields; Visual Pathways; Visual attention; Work; active vision; area striata; awake; base; career; career development; central visual field; computerized tools; data-driven model; design; extrastriate; extrastriate visual cortex; flexibility; fovea centralis; interest; lens; neural model; neuromechanism; neurophysiology; new technology; novel; professor; receptive field; relating to nervous system; repaired; response; retinal imaging; sample fixation; sensor; skills; spatial vision; spatiotemporal; statistics; tool; visual information; visual neuroscience; visual process; visual processing; visual tracking

Neural mechanisms of active vision in the fovea In many ways human vision is like a camera, with a lens that forms an image on a spatially arranged sensor (the retina). However, it is unlike a camera because the sensor has uneven sampling and is constantly moving with the eyes. Recent behavioral and theoretical work suggest these eye movements serve a faciliatory role in high acuity vision – where the eye movements are part of the computations and enhance spatial resolution. However, the neurophysiological mechanisms to support this facilitation remain unknown. More broadly, little is known about the neural mechanisms that integrate across the retinal motion generated by eye movements, especially in the central visual field (the fovea). This is particularly important because over 8 million Americans suffer from central vision loss due to retinal disorders. Even if the retinal signals could be repaired, it is imperative to understand how the brain reads out foveal signals to ensure recovery of high-acuity visual processing, and fixational eye movements are a part of that process. The proposed career development plan aims to address these questions by measuring visual processing in the foveal representation of primary visual cortex (V1) during natural visual behavior. This proposal uses custom high-resolution eye-tracking, a novel visual foraging paradigm, largescale neurophysiology, and state-of-the-art machine learning to make these measurements possible. The proposed research will not only generate fundamental understanding of how eye-movements facilitate visual processing, but also will integrate the experimental and theoretical tools required to support neurophysiological studies of active visual processing without a loss of rigor or detail. The candidate has extensive expertise in awake- behaving neurophysiology and computational modeling and the training plan is designed to support his further training in statistical modeling, high-resolution eye-tracking, and modern machine-learning techniques for analyzing neural population data. The primary mentor, Dr. Daniel Butts, is a world expert in statistical models of neural activity during active vision; Co-mentor, Dr. Michele Rucci, is a world leader in high-resolution eye tracking and theoretical approaches to active vision; and Co-mentor, Dr. Jude Mitchell, is a pioneer in establishing the marmoset model of visual neuroscience and an expert in neurophysiology of visual attention. Together, they will provide the guidance to establish the candidate’s transition to a successful independent research career.

中心凹主动视觉的神经机制 在许多方面，人类视觉就像一台相机，它有一个透镜，在空间上形成图像。 视网膜（Retina）然而，它与相机不同，因为传感器具有不均匀的 采样并随着眼睛不断移动。最近的行为和理论研究表明 这些眼球运动在高敏锐度视觉中起促进作用， 是计算的一部分并增强空间分辨率。然而，神经生理学 支持这种便利的机制仍然未知。更广泛地说，人们对 整合了眼球运动产生的视网膜运动的神经机制， 尤其是在中央视野（中央凹）中。这一点尤为重要，因为超过8 数百万美国人因视网膜疾病而遭受中心视力丧失。即使视网膜发出信号 可以修复，必须了解大脑如何读取中央凹信号，以确保 高敏锐度视觉处理的恢复和注视性眼球运动是该过程的一部分。 拟议的职业发展计划旨在解决这些问题， 在自然视觉期间初级视皮层（V1）的中央凹代表中的处理 行为该提案使用定制的高分辨率眼动跟踪，一种新颖的视觉觅食 范式，大规模神经生理学和最先进的机器学习，使这些 测量可能。这项研究不仅将产生基本的 了解眼球运动如何促进视觉处理，但也将整合 支持主动视觉神经生理学研究的实验和理论工具 不损失严谨性或细节的处理。候选人在清醒方面有丰富的经验- 行为神经生理学和计算建模，训练计划旨在 支持他在统计建模，高分辨率眼动跟踪和现代 用于分析神经群体数据的机器学习技术。主要导师，丹尼尔博士 巴茨博士是主动视觉期间神经活动统计模型的世界专家; Michele Rucci是高分辨率眼动追踪和理论方法的世界领导者， 主动视觉;共同导师Jude Mitchell博士是建立绒猴模型的先驱 他是视觉神经科学和视觉注意力神经生理学方面的专家。他们将共同 提供指导，以建立候选人的过渡到一个成功的独立研究 事业