KDI: Learning of Objects and Object Classes in Visual Cortex

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。这位研究人员和他的同事们解决了视觉皮质如何通过计算、生理和心理物理方法相结合的方式来学习表示和识别新对象和对象类别的问题。对警觉猴子的生理实验依赖于多电极记录，研究人员为这些记录开发了一个适当的数据挖掘技术工具包，部分是基于他们自己在学习和分类算法方面的工作。具体地说，研究人员开展了一个由四个相互作用的部分组成的多学科研究项目：i)颞下(IT)皮质神经元的计算建模，扩展了他们之前在IT中表示单个对象的工作；ii)清醒状态下使用多个电极的皮质生理学，表现为猴子在新类别刺激上接受类间和类内分类任务的训练；iii)处理多电极数据的新数据挖掘技术，包括分类和学习技术；4)视觉心理物理学，包括人类和猴子的功能核磁共振研究，允许将猴子生理学的发现与人脑中的物体学习联系起来。理解人脑中的学习意味着理解智力的核心。这不仅是科学界剩余的根本性挑战之一，而且也是一个即使是很小的进步也将对理解神经疾病和紊乱产生重大影响的领域，也将对计算和机器智能的未来产生重大影响。然而，尽管在过去的十年或二十年里取得了巨大的进步，科学仍然不知道大脑皮层的不同区域起什么作用以及如何起作用。因为理解大脑，这是我们所知道的最复杂的系统，是一项巨大的努力，本项目专注于理解大脑皮层的一部分，参与每个人日常生活中的一项关键和非常困难的任务--即使主观上非常容易：学习分类和识别视觉对象，如面孔或汽车。了解脑细胞是如何代表物体的，将是神经科学的重大突破，也是最终设计能够实现与人类相似的性能的机器的重大突破。更重要的是，物体识别这一特定问题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的学习和智能等更广泛的问题打开大门。

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.