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.

POGGIO9872936学习对象进行分类和识别的能力是人类和高等动物视觉系统的关键特征。 然而，这些强大的视觉能力的基础表示和获得的表现仍然在很大程度上尚不清楚。 研究者及其同事解决了视觉皮层如何通过计算，生理和心理物理学方法结合使用新颖的对象和对象类别的问题。 警报猴子上的生理实验依赖于多电极记录，研究人员为此开发了适当的数据挖掘技术的工具包，部分基于他们自己的学习和分类算法的工作。 特别是，研究人员进行了一个由四个相互作用组成部分组成的多学科研究项目：i）地下颞（IT）皮质神经元的计算建模，将其先前的工作扩展到其中单个对象的表示； ii）在清醒中使用多个电极的皮质生理学，在新颖的刺激类别中进行了培训的猴子，经过培训的猴子； iii）用于处理多个电极数据的新数据挖掘技术，包括分类和学习技术； iv）视觉心理物理学，包括人类和猴子的功能磁共振成像研究，允许将猴子生理学的发现与人脑中的对象学习联系在一起。理解人脑中的学习意味着了解智力的核心。 这不仅是科学中剩下的基本挑战之一，而且还是一个领域，即使是向前迈出的一小步也将对理解神经疾病和疾病以及计算和机器智能的未来产生重大影响。 但是，尽管过去十年或两年来取得了巨大进展，但科学尚不知道皮质的各个领域以及如何做。 因为理解大脑，我们知道的最复杂的系统是一项巨大的努力，因此本项目着重于理解皮层的一部分，涉及每个人日常生活中的关键和非常困难的任务 - 即使主观上非常容易：学会对面孔或汽车等视觉对象进行分类和识别。 了解脑细胞如何代表物体将是神经科学的重大突破，也是最终设计能够实现人类表现的机器。 更重要的是，在特定对象识别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学习和智能的更广泛问题打开大门。