CRCNS: Neural Reprensentation of Hierarchical Visual Concepts in Natural Scenes

描述（由申请人提供）：自然图像的复杂性可能是巨大的：一张小（100 × 100像素）的图片可以描述的可能图像的数量实际上是无限的（10000256），比人类在其整个存在期间所见过的所有图像都要多。任何系统如何处理如此规模的输入数据，并根据世界上大约20万个物体、它们的空间布局和场景结构来解释/理解这些数据？然而，这是一项人类视觉系统通常在几分之一秒内完成的任务。秘密一定在于自然图像是高度冗余的，生活在这个几乎无限可能性的宇宙中的有限空间中，哺乳动物的视觉系统已经发现并利用了这一事实。特别地，我们推测神经元和群体会被自然图像的统计结构所调整，例如，基于先前的工作，稀疏编码思想可以帮助预测视觉皮层中“简单细胞”的接受野特性。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。这将产生一个刺激字典，它将被用作研究神经元和神经元群体的调谐特性的刺激，以及一个视觉概念字典，它将被用来预测调谐特性。其次，我们将进行多电极神经生理学研究神经元的调谐特性，以及神经元群体，在不同层次的视觉皮层响应刺激字典。第三，我们将执行数据分析，使用模型驱动和数据驱动方法的组合来模拟神经元和种群的调谐特性，模型驱动假设神经元被调谐到图像的统计特性，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-用于学习刺激字典，视觉概念和执行数据分析-基于统计和机器学习技术。这些假设数据的分层组成结构提供了驯服自然图像复杂性的可能性，并且也与已知的视觉皮层的分层结构一致。智力优势：这项研究将有助于理解自然图像的结构，确定视觉皮层神经元调谐特性的模型，并开发新的数据分析技术。它有可能显著推进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，包括人口水平。这将有助于更好地理解视觉皮层，也有助于计算机视觉系统的发展。更广泛的影响：该项目本质上是跨学科的，应该在多个学科中产生广泛的影响：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。了解视觉皮层中的神经元特性是为视障人士开发治疗方法和修复装置的临床事业的先决条件。拟议的研究项目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，以及CMU神经计算的跨学院本科生辅修项目，研究人员正在各自的大学开发这些项目。研究人员还计划在NIPS、COSYNE组织讲习班，并将他们的研究纳入各自大学的本科和研究生课程。这项工作也将影响到其他学院的本科生，通过匹兹堡的一个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，我们将在IPAM （UCLA）举办工作坊和暑期学校。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。这些pi在吸引本科生（包括女性和少数族裔）参与其nsf资助的研究方面有着良好的记录，并将继续努力培养下一代计算神经科学家。

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.