CRCNS: Spatiotemporal Scene Statistics and Contextual Influences in Vision

CRCNS：视觉中的时空场景统计和上下文影响

• 批准号: 8305755
• 负责人: ADAM KOHN
• 金额: $ 27.89万
• 依托单位: ALBERT EINSTEIN COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8305755
• 关键词: Area; Arts; Cells; Collaborations; Communities; Complex; Computational algorithm; Computer Simulation; Data; Dependency; Discipline; Fostering; Future; Goals; Image; Joints; Measures; Mentors; Methods; Modeling; Neurons; Neurosciences; Physiology; Population; Postdoctoral Fellow; Process; Research; Research Personnel; Scientist; Solid; Stimulus; Students; Study models; System; Technology; Time; Training; Vision; Visual; Visual Aid; Visual Cortex; Visual system structure; Work; area V2; area striata; base; computerized data processing; data sharing; design; extrastriate visual cortex; high school; image processing; insight; model development; movie; neurophysiology; outreach; relating to nervous system; research study; response; sensory system; spatiotemporal; statistics; theories; visual neuroscience; web site

DESCRIPTION (provided by applicant): Intellectual merit: A central question in neuroscience is understanding how cortical networks process complex natural stimuli. Neurophysiological studies and computational models have traditionally focused on simple stimuli, such as gratings and bars. While providing important insights, it is difficult to extrapolate from these studies to an understanding of the processing of more natural input. On the other hand, a main hurdle to making progress in the field is that natural scenes are complex and it is not clear what it is about a given scene that evokes a given neural response. To overcome this limitation and to push forward our understanding of cortical processing of natural inputs, we will make use of recent advances in understanding natural scene statistics to closely integrate theory and neurophysiological experiments. We posit that a key factor distinguishing natural images and movies from random scenes are joint statistical dependencies in space and time. Further, we hypothesize that visual neurons are sensitive to these dependencies. We will build a unified modeling framework of spatiotemporal contextual effects in neurons, which is determined by the statistical dependencies in scenes. Importantly, the predictions of the model will be used to guide neurophysiology experiments and to interpret the results. Using natural stimuli, we will measure effects of spatial, temporal, and spatiotemporal context in single neurons and in populations of cells, including determining how interactions between neurons contribute to contextual effects. We will record in primary visual cortex (V1) because it provides a solid background on which to base our experiments. We will conduct parallel recordings in extrastriate area V2 because previous work suggests that it may have different sensitivity to contextual information. The experimental results will validate and guide the modeling framework. Our approach will be a significant advance over previous scene statistics modeling work that has focused on explaining limited contextual physiology data for simple stimuli such as gratings, and will for the first time make full use of the power of scene statistics to answer a fundamental question. Most importantly, our work will make significant strides in elucidating how cortical circuits process natural scenes, within a theoretical framework that provides both predictive and explanatory power. Collaboration: The project will involve a collaborative effort between two young investigators with expertise in computational visual neuroscience and systems physiology; it combines state-ofthe- art algorithms from computational vision and technology for recording populations of neurons in early visual cortex. We will achieve our goal by closely integrating theory and model development with electrophysiological experiments, an approach fostered by the proximity of the two investigators. Broader Impacts: This proposal is expected to have broad impacts in five main areas. First, the work will have broad impact for basic, biomedical, and applied disciplines, including: studying other sensory systems under natural input; building superior visual aids; designing artificial systems; and advancing image and signal processing. Second, the data and stimuli will be made broadly available to the community through the CRCNS data sharing website. Third, the project will be used to train and mentor postdoctoral fellows to become independent research scientists. Fourth, the project will for the first time introduce students at Albert Einstein to the combination of theoretical and experimental approaches for solving fundamental questions in neuroscience. Finally, the project will be used as part of an outreach effort to expose local underrepresented high school students in the Bronx to exciting scientific research.

描述（由申请人提供）： 智力优点：神经科学的一个核心问题是理解皮层网络如何处理复杂的自然刺激。神经生理学研究和计算模型传统上集中于简单的刺激，例如光栅和条形。虽然提供了重要的见解，但很难从这些研究中推断出对更自然输入的处理的理解。另一方面，该领域取得进展的一个主要障碍是自然场景很复杂，并且不清楚特定场景是什么引起了特定的神经反应。为了克服这一限制并推进我们对自然输入的皮层处理的理解，我们将利用理解自然场景统计方面的最新进展，将理论和神经生理学实验紧密结合起来。我们假设区分自然图像和电影与随机场景的关键因素是空间和时间上的联合统计依赖性。此外，我们假设视觉神经元对这些依赖性敏感。我们将建立一个统一的神经元时空情境效应建模框架，该模型由场景中的统计依赖性决定。重要的是，模型的预测将用于指导神经生理学实验并解释结果。利用自然刺激，我们将测量单个神经元和细胞群中空间、时间和时空背景的影响，包括确定神经元之间的相互作用如何影响背景效应。我们将在初级视觉皮层（V1）中进行记录，因为它为我们的实验提供了坚实的背景。我们将在纹外区域V2进行并行记录，因为之前的工作表明它可能对上下文信息有不同的敏感性。实验结果将验证和指导建模框架。我们的方法将比以前的场景统计建模工作取得重大进步，之前的场景统计建模工作侧重于解释简单刺激（例如光栅）的有限上下文生理学数据，并且将首次充分利用场景统计的力量来回答基本问题。最重要的是，我们的工作将在提供预测和解释能力的理论框架内，在阐明皮层回路如何处理自然场景方面取得重大进展。合作：该项目将涉及两位在计算视觉神经科学和系统生理学方面具有专业知识的年轻研究人员之间的合作；它结合了最先进的计算视觉算法和记录早期视觉皮层神经元群体的技术。我们将通过将理论和模型开发与电生理学实验紧密结合来实现我们的目标，这是两位研究人员的密切关系所促成的方法。更广泛的影响：该提案预计将在五个主要领域产生广泛影响。首先，这项工作将对基础、生物医学和应用学科产生广泛影响，包括：研究自然输入下的其他感觉系统；建立卓越的视觉辅助工具；设计人工系统；并推进图像和信号处理。其次，数据和刺激将通过 CRCNS 数据共享网站向社区广泛提供。第三，该项目将用于培训和指导博士后研究员成为独立研究科学家。第四，该项目将首次向阿尔伯特·爱因斯坦的学生介绍理论和实验方法相结合的方法来解决神经科学的基本问题。最后，该项目将作为外展工作的一部分，让布朗克斯当地代表性不足的高中生接触到令人兴奋的科学研究。