Computational Models and Physiological Studies of Feedback in Visual Object Recognition Tasks

视觉对象识别任务中反馈的计算模型和生理学研究

• 批准号: 0640097
• 负责人: Tomaso Poggio
• 金额: $ 47.57万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-04-15 至 2011-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0640097&HistoricalAwards=false
• 关键词: Computational; Models; Physiological; Studies; Feedback

Visual recognition of objects, scenes and people around us depends on the visual cortex, one of the largest and most complex parts of the primate brain. This area also presents one of the major puzzles in cognitive sciences: What is the role of the cortical back-projections, that is, links from the secondary areas back into the primary areas? In this project, Drs. Tomaso Poggio and Earl Miller try to interpret the neural computations underlying complex visual recognition tasks. Their studies will help to transfer knowledge gained from animal models of visual perception toward the understanding of higher cognitive visual processes in humans. A very recent computational theory of the primate object recognition system agrees with a variety of physiological findings in different visual areas, such as V1 (primary visual cortex), V4 (visual area 4), IT (inferotemporal cortex), and PFC (prefrontal cortex). Even more surprisingly, it mimics human behavioral performance on rapid categorization of complex natural images, and performs, as well as several state-of-the-art computer vision systems on difficult recognition tasks. Considering that the model is able to account for rapid object recognition, and that it currently only uses feedforward processing, a significant puzzle concerns the computational and functional role of the abundant anatomical back-projections known to exist in cortex. The proposed project takes a two-pronged approach toward finding their function. First, experiments with the computational model of the ventral stream will provide useful insights for understanding the possible functions of the back-projections. Second, experiments with multi-unit recordings in macaques will characterize top-down effects and their timing in several different recognition tasks at the level of IT and PFC. The analysis will make use of a recently developed automatic classification technique that relates brain activity with individual visual objects. By combining results from the modeling and experimentation, computational explanations for the cognitive role of the feedback processing can be tested. Understanding the function of back-projections in vision will help us understand the neural basis of vision itself, but it will also help us understand the global design of the brain. The intricacy of this organ continues to amaze us, and getting a handle on its mechanism through a relatively well-understood area like vision is a promising avenue for expanding our appreciation of the whole system. A further goal is to show that the interaction between computational theories, in particular of vision, and experiments will make it easier to comprehend brain functions. Such advances help us appreciate the normal function of the brain and allow us better means of helping when it does not function normally, of making machines see better, and of bringing new approaches to robotics.

我们对周围物体、场景和人的视觉识别取决于视觉皮层，视觉皮层是灵长类动物大脑中最大、最复杂的部分之一。该领域还提出了认知科学中的主要难题之一：皮质反投影（即从次要区域回到主要区域的链接）的作用是什么？在这个项目中，博士。 Tomaso Poggio 和 Earl Miller 试图解释复杂视觉识别任务背后的神经计算。他们的研究将有助于将从动物视觉感知模型中获得的知识转移到对人类更高认知视觉过程的理解。灵长类物体识别系统的最新计算理论与不同视觉区域的各种生理学发现一致，例如 V1（初级视觉皮层）、V4（视觉区域 4）、IT（下颞叶皮层）和 PFC（前额叶皮层） ）。更令人惊讶的是，它在复杂自然图像的快速分类方面模仿了人类的行为表现，并且在困难的识别任务上的表现与几种最先进的计算机视觉系统一样。考虑到该模型能够快速识别物体，并且目前仅使用前馈处理，因此一个重要的难题涉及已知存在于皮层中的丰富的解剖反投影的计算和功能作用。拟议的项目采取了双管齐下的方法来寻找它们的功能。首先，腹侧流计算模型的实验将为理解反投影的可能功能提供有用的见解。其次，在猕猴身上进行的多单元记录实验将描述自上而下的效应及其在 IT 和 PFC 级别的几种不同识别任务中的时间安排。 该分析将利用最近开发的自动分类技术，将大脑活动与单个视觉对象联系起来。通过结合建模和实验的结果，可以测试反馈处理的认知作用的计算解释。了解反投影在视觉中的功能将有助于我们了解视觉本身的神经基础，同时也将有助于我们了解大脑的整体设计。这个器官的复杂性继续让我们感到惊讶，通过视觉等相对容易理解的领域来掌握它的机制是扩大我们对整个系统认识的一个有希望的途径。 进一步的目标是表明计算理论（特别是视觉理论）与实验之间的相互作用将使理解大脑功能变得更容易。 这些进步帮助我们了解大脑的正常功能，并让我们能够在大脑功能不正常时提供更好的帮助，让机器看得更清楚，并为机器人技术带来新的方法。