Cortical computations underlying binocular motion integration

双目运动集成的皮层计算

• 批准号: 10188534
• 负责人: Wyeth Daniel Bair
• 金额: $ 38.75万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-30 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10188534
• 关键词: 3-Dimensional; Affect; Architecture; Biological; Biomedical Engineering; Brain; Collaborations; Color; Complex; Computer Models; Computer Vision Systems; Cues; Data; Data Analyses; Development; Devices; Disadvantaged; Discrimination; Disease; Electrodes; Electrophysiology (science); Elements; Foundations; Frequencies; Functional disorder; Human; Joints; Knowledge; Link; Literature; Medicine; Modeling; Motion; Neurons; Neurosciences; Noise; Output; Pathway interactions; Pattern; Performance; Physiological; Physiology; Population; Primates; Production; Psychophysics; Reproducibility; Research; Role; Scientist; Signal Transduction; Stimulus; System; Technology; Testing; Time; Vision; Vision Disparity; Visual; Visual Cortex; Visual Motion; Visual Perception; area MT; base; cloud based; color processing; disability; experimental study; extrastriate visual cortex; fitness; improved; in vivo; insight; interest; neural circuit; neurophysiology; novel; predictive modeling; relating to nervous system; response; spatial integration; spatiotemporal; theories; tool; visual neuroscience; visual process; visual processing; visual stimulus

PROJECT SUMMARY / ABSTRACT Neuroscience is highly specialized—even visual submodalities such as motion, depth, form and color processing are often studied in isolation. One disadvantage of this isolation is that results from each subfield are not brought together to constrain common underlying neural circuitry. Yet, to understand the cortical computations that support vision, it is important to unify our fragmentary models that capture isolated insights across visual submodalities so that all relevant experimental and theoretical efforts can benefit from the most powerful and robust models that can be achieved. This proposal aims to take the first concrete step in that direction by unifying models of direction selectivity, binocular disparity selectivity and 3D motion selectivity (also known as motion-in-depth) to reveal circuits and understand computations from V1 to area MT. Motion in 3D inherently bridges visual submodalities, necessitating the integration of motion and binocular processing, and we are motivated by two recent paradigm-breaking physiological studies that have shown that area MT has a robust representation of 3D motion. In Aim 1, we will create the first unified model and understanding of the relationship between pattern and 3D motion in MT. In Aim 2, we will construct the first unified model of motion and disparity processing in MT. In Aim 3, we will develop a large-scale biologically plausible model of these selectivities that represents realistic response distributions across an MT population. Having a population output that is complete enough to represent widely-used visual stimuli will amplify our ability to link to population read-out theories and to link to results from psychophysical studies of visual perception. Key elements of our approach are (1) an iterative loop between modeling and electrophysiological experiments; (2) building a set of shared models, stimuli, data and analysis tools in a cloud-based system that unifies efforts across labs, creating opportunities for deep collaboration between labs that specialize in relevant submodalities, and encouraging all interested scientists to contribute and benefit; (3) using model-driven experiments to answer open, inter-related questions that involve motion and binocular processing, including motion opponency, spatial integration, binocular integration and the timely problem of how 3D motion is represented in area MT; (4) unifying insights from filter-based models and conceptual, i.e., non-image- computable, models to generate the first large-scale spiking hierarchical circuits that predict and explain how correlated signals and noise are transformed across multiple cortical stages to carry out essential visual computations; and (5) carrying out novel simultaneous recordings across visual areas. This research also has potential long-term benefits in medicine and technology. It will build fundamental knowledge about functional cortical circuitry that someday may be useful for interpreting dysfunctions of the cortex or for helping biomedical engineers construct devices to interface to the brain. Insights gained from the visual cortex may also help to advance computer vision technology.

项目摘要/摘要 神经科学是高度专业化的--甚至是运动、深度、形状和颜色等视觉亚形态 加工通常是孤立地研究的。这种隔离的一个缺点是每个子字段都会产生 并不是聚集在一起来约束共同的潜在神经电路。然而，要理解大脑皮层 对于支持愿景的计算，重要的是统一我们捕获孤立见解的零散模型 跨视觉子模式，以便所有相关的实验和理论工作都可以从 可以实现的强大而健壮的模型。这项提议旨在迈出具体的第一步。 方向选择性、双目视差选择性和3D运动选择性的统一模型 (也称为深度运动)，以揭示电路并了解从V1到区域MT的计算。运动输入 3D固有地桥接视觉子模式，需要运动和双目处理的集成， 我们受到最近两项打破范式的生理学研究的鼓舞，这些研究表明，MT区 具有3D运动的强健表示。在目标1中，我们将创建第一个统一的模型和对 MT中图案与3D运动的关系。在目标2中，我们将构建第一个统一的模型 机器翻译中的运动和视差处理。在目标3中，我们将开发一个大规模的生物学上可信的模型 这些选择性代表了MT人群中真实的响应分布。拥有一个 足以代表广泛使用的视觉刺激的群体输出将增强我们连接的能力 到人口读出理论，并链接到视觉知觉的心理物理学研究的结果。钥匙 我们方法的要素是(1)建模和电生理实验之间的迭代循环；(2) 在统一努力的基于云的系统中构建一组共享的模型、激励、数据和分析工具 跨实验室，为专门从事相关研究的实验室之间的深度协作创造机会 子模式，并鼓励所有感兴趣的科学家做出贡献并受益；(3)使用模型驱动 回答涉及运动和双目处理的开放的、相互关联的问题的实验，包括 运动对抗、空间整合、双目整合和3D运动如何的及时性问题 在MT区域表示；(4)统一基于过滤器的模型和概念性模型的见解，即非图像- 可计算的模型，用于生成第一个大规模尖峰分层电路，用于预测和解释 相关信号和噪声跨越多个大脑皮层阶段进行变换，以执行基本视觉 计算；以及(5)跨可视区域进行新颖的同时记录。这项研究也有 在医学和技术方面的潜在长期利益。它将建立关于泛函的基础知识 皮质回路，有朝一日可能有助于解释皮质功能障碍或帮助 生物医学工程师构建了与大脑连接的设备。从视觉皮质获得的洞察力可能 也有助于推动计算机视觉技术的发展。