Abstract: The mammalian brain is a metabolically expensive device, and evolutionary pressures have presumably driven it to make productive use of its resources. In early stages of sensory processing, this concept can be expressed more formally as an optimality principle: the brain maximizes the information that is encoded about relevant sensory variables, given available resources. I'll describe a specific instantiation of this hypothesis that predicts a direct relationship between the distribution of sensory attributes encountered in the environment, and the selectivity and response levels of neurons within a population that encodes those attributes. This allocation of neural resources, in turn, imposes direct limitations on the ability of the organism to discriminate different values of the encoded attribute. I'll show that these physiological and perceptual predictions are borne out in a variety of visual and auditory attributes. Finally, I'll show that this encoding of sensory information provides a natural substrate for subsequent computation, which can make use of the knowledge of environmental (prior) distributions that is embedded in the population structure.
Biography: Dr. Simoncelli is an investigator with the Howard Hughes Medical Institute and a Professor of Neural Science, Mathematics, and Psychology at New York University. He began his higher education as a physics major at Harvard, went to Cambridge University on a Knox Fellowship to study mathematics for a year and a half, and earned a doctorate in electrical engineering and computer science at the Massachusetts Institute of Technology. He then joined the faculty of the Computer and Information Science Department at the University of Pennsylvania. In 1996, he moved to NYU as part of the Sloan Center for Theoretical Visual Neuroscience.