CRCNS: Neural Reprensentation of Hierarchical Visual Concepts in Natural Scenes

CRCNS：自然场景中分层视觉概念的神经表示

• 批准号: 8312499
• 负责人: TAI-SING LEE
• 金额: $ 35.74万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8312499
• 关键词: Affect; Appearance; Biological; Cells; Clinical; Code; Computer Vision Systems; Data; Data Analyses; Development; Diagnostic; Dictionary; Dimensions; Discipline; Educational Curriculum; Educational workshop; Electrodes; Entropy; Graph; Human; Image; International; Intuition; Investigation; Knowledge; Lead; Learning; Letters; Life; Machine Learning; Measures; Methods; Minor; Minority; Modeling; Nature; Neurons; Neurosciences; Noise; Pattern; Peer Review; Physiological; Population; Primates; Probability; Process; Property; Prosthesis; Publications; Qatar; Race; Research; Research Personnel; Sampling; Schools; Staging; Statistical Models; Stimulus; Structure; System; Techniques; Testing; Texture; Therapeutic; Time; Training; Training Programs; Universities; Vision; Visual; Visual Cortex; Visual system structure; Vocabulary; Woman; Work; base; cognitive neuroscience; college; computational neuroscience; design; devices for the visually impaired; isophosphamide mustard; lectures; neuromechanism; neurophysiology; next generation; novel; programs; receptive field; relating to nervous system; research study; response; statistics; tool; undergraduate student; visual stimulus

DESCRIPTION (provided by applicant): The complexity of natural images is potentially enormous: the number of possible images that can be described by a smallish (100 by 100 pixels) picture is practically infinite (10000256), more than all the images the human race has ever witnessed during its entire existence. How can any system process input data of this magnitude of dimensions and interpret/understand it in terms of the estimated 200,000 objects in the world, their spatial layouts, and scene structures? Yet, this is a task that human visual systems routinely perform in a fraction of a second. The secret must lie in the fact that natural images are highly redundant, living in a restricted space inside this universe of almost infinite possibilities, and that mammalian visual systems have discovered and exploited this fact. In particular, we conjecture that neurons and populations are tuned to the statistical structure of natural images, building on previous work showing, for example, that sparse coding ideas can help predict receptive field properties of 'simple cells' in the visual cortex. This proposal has three stages. Firstly, we will perform a statistical analysis of natural images to classify and model the types of visual patches that appear. This will result in a stimulus dictionary, which will be used as stimuli to investigate the tuning properties of neurons and neuronal populations, and a visual concept dictionary which will be used to make predictions for the tuning properties. Secondly, we will perform multielectrode neurophysiological investigation of the tuning properties of neurons, and neuron populations, at different levels of the visual cortex in response to the stimulus dictionary. Thirdly, we will perform data analysis to model the tuning properties of neurons and populations using a combination of model-driven, which assumes that neurons are tuned to statistical properties of images, and data-driven approaches which can be thought of as learning 'neural visual concepts' directly from the neuron's response to the stimuli. Our theoretical approach - for learning the stimuli dictionary, the visual concepts, and performing data analysis - is based on statistical and machine learning techniques. These assume a hierarchical compositional structure for the data which offers the possibility of taming the complexity of natural images and is also consistent with the known hierarchical structure of the visual cortex. Intellectual merit: This research will help understand the structure of natural images, determine models for the tuning properties of neurons in the visual cortex, and develop novel data analysis techniques. It has the potential to significantly advance our understanding of the statistical structures of natural images and the neural encoding of these structures, including the population level. This will lead to greater understanding of the visual cortex and also help the development of computer vision systems. Broader impacts: This project is interdisciplinary in nature and should have broad impact in multiple disciplines: neuroscience and biological vision, statistical neural data analysis, computer vision, and machine learning. Understanding neuronal properties in the visual cortex is a pre-requisite to the clinical enterprise of developing therapeutic methods and prosthetic devices for the visually impaired. The proposed research program will help facilitate a new graduate program in Computational and Cognitive Neuroscience at UCLA, an inter-college undergraduate minor in Neural Computation at CMU that the investigators are developing at their respective universities. The investigators also plan to organize workshops in NIPS, COSYNE, as well as to integrate their research into both undergraduate and graduate curriculum in their respective universities. This work will also affect undergraduate students at other colleges, by a summer undergraduate training program in Pittsburgh, another at CMU's Qatar campus. In addition, we will propose a workshop and summer school at IPAM (UCLA). We anticipate that this research will lead to invited lectures, peer reviewed publications and, if successful, will have national and international impact. The PIs have good track record in involving undergraduates, including women and minorities, in their NSF-sponsored research, and will continue to endeavor in the training of the next generation of computational neuroscientists.

描述(申请人提供)：自然图像的复杂性可能是巨大的：一幅较小(100x100像素)的图片可以描述的可能图像的数量几乎是无限的(10000256)，超过了人类在其整个存在过程中所看到的所有图像。任何系统如何处理这种规模的输入数据，并根据世界上估计的200,000个对象、它们的空间布局和场景结构来解释/理解它？然而，这是人类视觉系统通常在不到一秒的时间内完成的任务。秘密一定在于这样一个事实，即自然图像是高度冗余的，生活在这个几乎无限可能的宇宙中的一个有限的空间里，而哺乳动物的视觉系统已经发现并利用了这一事实。特别是，我们推测神经元和群体与自然图像的统计结构是一致的，这是基于之前的工作，例如，稀疏编码思想可以帮助预测视觉皮质中“简单细胞”的感受野特性。这项提议分为三个阶段。首先，我们将对自然图像进行统计分析，对出现的视觉斑块类型进行分类和建模。这将产生一个刺激词典，它将被用作研究神经元和神经元群体的调谐特性的刺激，以及一个视觉概念词典，它将被用来对调谐特性进行预测。其次，我们将进行多电极神经生理学研究，研究视皮层不同水平的神经元和神经元群体对刺激词典的反应。第三，我们将执行数据分析，以使用模型驱动和数据驱动方法的组合来对神经元和种群的调节特性进行建模，模型驱动假定神经元被调节到图像的统计特性，数据驱动方法可以被认为是直接从神经元对刺激的反应中学习“神经视觉概念”。我们的理论方法--学习刺激词典、视觉概念和进行数据分析--基于统计和机器学习技术。这些假设了数据的层次组成结构，这提供了驯服自然图像的复杂性的可能性，并且也与已知的视觉皮质的层次结构一致。智力优势：这项研究将有助于理解自然图像的结构，确定视觉皮质神经元调谐特性的模型，并开发新的数据分析技术。它有可能极大地提高我们对自然图像的统计结构和这些结构的神经编码的理解，包括种群水平。这将导致对视觉皮质的更多了解，也有助于计算机视觉系统的发展。更广泛的影响：这个项目本质上是跨学科的，应该在多个学科产生广泛影响：神经科学和生物视觉、统计神经数据分析、计算机视觉和机器学习。了解视皮层的神经元特性是为视障患者开发治疗方法和假体设备的临床事业的先决条件。拟议的研究计划将有助于促进加州大学洛杉矶分校计算和认知神经科学的新研究生项目，这是CMU神经计算的跨学院本科生辅修课程，研究人员正在各自的大学开发。调查人员还计划在NIP和COSYNE组织讲习班，并将他们的研究纳入各自大学的本科生和研究生课程。这项工作也将影响其他大学的本科生，通过匹兹堡的夏季本科生培训项目，以及CMU卡塔尔校区的另一个项目。此外，我们还将提议在加州大学洛杉矶分校(IPAM)举办工作坊和暑期班。我们预计，这项研究将导致邀请讲座，同行评议的出版物，如果成功，将产生国内和国际影响。PIs在包括女性和少数族裔在内的本科生参与NSF赞助的研究方面有着良好的记录，并将继续努力培训下一代计算神经科学家。