CAREER:Deciphering the Neural Code From Perception To Cognition

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Human beings can recognize objects in a highly selective, robust and fast manner. One of the remarkable properties of our recognition machinery is the possibility to selectively recognize objects even after transformations such as rotation, translation, scaling, occlusion and clutter. How the human brain accomplishes recognition is not well understood. More is known about processing of sensory information from receptors to the initial stages in cortex than about the subsequent transformation of perceptual data into cognition. In parallel, over the last decades, major progress has been made in building ever more accurate and sophisticated computers and devices to capture sensory information but progress has been slower in terms of developing algorithms and hardware to automatically interpret the sensory data. With support from the National Science Foundation, Dr. Gabriel Krieman is undertaking research whose aim is to elucidate the computational steps and algorithms implemented by the cerebral cortex to transform incoming inputs into cognitive functions and behavior. The research focuses on one particular aspect of cognitive experience, the neuronal mechanisms and circuits that underlie visual processing. While vision is only one of many aspects of cognition, lessons learnt from studying visual cortex can also eventually help describe other aspects of cortical function and can pave the way for research on other challenging aspects of cognition. To investigate visual cognition, Dr. Kreiman takes advantage of a rare opportunity to both stimulate and record electrical activity at high spatial and temporal resolution directly from the human brain in epilepsy patients. The study investigates tasks where visual cognition is dissociated from the incoming sensory processing in order to isolate the cognitive operations involved in recognition. The discoveries about the function of biological neural circuits will be applied to develop biophysically-inspired robust machine vision algorithms. Visual recognition is essential for most everyday tasks including navigating, reading, and identifying objects, faces and emotions. By furthering our understanding of the transformation of perceptual information into cognition, the study is contributing to two broad goals: (1) Helping to alleviate the challenging conditions that involve cognitive disorders through the development of brain-machine interfaces; and (2) Applying knowledge about neuronal circuits to develop computational algorithms to extract cognitive information from sensory data. Building a fast, robust and reliable artificial vision system would have profound repercussions in many areas of science and engineering including pattern recognition, surveillance and security, automatic navigation, clinical image analysis and others. These scientific and engineering advances could in turn translate into important real-world applications of interest for industrial partnerships. Dr. Kreiman pursues these goals by studying the best possible system that can solve visual recognition challenges, the human brain. Understanding the visual system relies on many skills ranging from computer science to engineering to physics to neuroscience to psychology. The project serves well to train a generation of multidisciplinary students who can build on the fundamental science knowledge and apply this knowledge to challenging biological problems.