KDI: Learning of Objects and Object Classes in Visual Cortex

KDI：视觉皮层中对象和对象类的学习

• 批准号: 9872936
• 负责人: Tomaso Poggio
• 金额: $ 100万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-10-15 至 2002-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9872936&HistoricalAwards=false
• 关键词: KDI; Learning; Objects; Object; Classes

Poggio9872936The ability to learn to categorize and recognize objects is a key feature of the visual system of humans and higher animals. Yet, how the representation underlying these powerful visual abilities is organized and acquired is still largely unknown. The investigator and his colleagues tackle the problem of how the visual cortex learns to represent and recognize novel objects and object classes through a combination of computational, physiological and psychophysical approaches. The physiological experiments on alert monkeys rely on multielectrode recordings for which the investigators develop a tool kit of appropriate data mining techniques, based in part on their own work on learning and classification algorithms. In particular, the investigators undertake a multi-disciplinary research project consisting of four interacting components: i) Computational modeling of inferotemporal (IT) cortical neurons, extending their previous work on representations of single objects in IT; ii) cortical physiology using multiple electrodes in awake, behaving monkeys trained on between- and within-class classification tasks on novel classes of stimuli; iii) new data mining techniques for processing multiple electrode data, including classification and learning techniques; iv) visual psychophysics including fMRI studies in humans and monkeys, allowing to relate the findings from monkey physiology to object learning in the human brain.Understanding learning in the human brain means understanding the very core of intelligence. Not only is this one of the remaining fundamental challenges in science but it is also one area where even small steps forward will have significant implications for understanding neurological diseases and disorders, and also for the future of computing and machine intelligence. However, despite enormous progress in the last decade or two, science does not yet know what various areas of the cortex do and how. Because understanding the brain, the most complex system we know, is a huge endeavor, the present project focuses on understanding a part of cortex, involved in a key and very difficult task in everybody's daily life -- even if subjectively very easy: learning to categorize and recognize visual objects such as faces or cars. Understanding how brain cells come to represent objects will be a major breakthrough for neuroscience and also for eventually designing machines capable of achieving human-like performance. More importantly, any significant progress in the specific problem of object recognition will have a major impact on the goals of the KDI program, because it will open the door to understanding broader issues of learning and intelligence in brains and machines.

学习分类和识别物体的能力是人类和高等动物视觉系统的一个关键特征。 然而，这些强大的视觉能力背后的表征是如何组织和获得的，在很大程度上仍然是未知的。 研究人员和他的同事们解决了视觉皮层如何通过计算，生理和心理物理方法的组合来学习表示和识别新物体和物体类别的问题。 对警觉猴子的生理实验依赖于多电极记录，研究人员开发了一套适当的数据挖掘技术工具包，部分基于他们自己在学习和分类算法方面的工作。 特别是，研究人员进行了一个多学科的研究项目，包括四个相互作用的组成部分：i）下颞（IT）皮层神经元的计算建模，扩展了他们以前在IT中单个物体的表征工作; ii）在清醒的行为猴子中使用多个电极进行皮层生理学训练，对新的刺激类别进行类间和类内分类任务;（3）新的数据挖掘技术，用于处理多电极数据，包括分类和学习技术;（4）视觉心理物理学，包括对人类和猴子的功能磁共振成像研究，将猴子的生理学发现与人类大脑的物体学习联系起来。理解人类大脑的学习意味着理解智力的核心。 这不仅是科学中剩余的基本挑战之一，而且也是一个即使是微小的进步也会对理解神经疾病和障碍以及计算和机器智能的未来产生重大影响的领域。 然而，尽管在过去的一二十年里取得了巨大的进步，科学仍然不知道大脑皮层的各个区域是做什么的，以及如何做的。 因为了解大脑，我们所知道的最复杂的系统，是一个巨大的奋进，目前的项目集中在了解皮层的一部分，涉及到一个关键的和非常困难的任务，在每个人的日常生活-即使主观上非常容易：学习分类和识别视觉对象，如人脸或汽车。 了解脑细胞如何来代表物体将是神经科学的一个重大突破，也是最终设计出能够实现人类性能的机器的一个重大突破。 更重要的是，在物体识别这一特定问题上的任何重大进展都将对KDI计划的目标产生重大影响，因为它将为理解大脑和机器中更广泛的学习和智能问题打开大门。