The 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.
