Embedding of Prior Probabilities in Neural Populations
Description
Abstract: The mammalian brain is a metabolically expensive device, and evolutionary pressures have presumably driven it to make productive use of its resources. For 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 (in particular, Bayesian estimation), which can make use of the knowledge of environmental (prior) distributions that is embedded in the population structure.
Speaker Bio
Eero P. Simoncelli} received the BS degree (summa cum laude) in physics from Harvard University in 1984 and the MS and PhD degrees in electrical engineering from Massachusetts Institute of Technology in 1988 and 1993, respectively. He studied applied mathematics at Cambridge University for a year and a half. He was an assistant professor in the Computer and Information Science Department at the University of Pennsylvania from 1993 to 1996. In September 1996, he moved to New York University, where he is currently a professor in neural science and mathematics. In August 2000, he became an investigator at the Howard Hughes Medical Institute under their new program in computational biology. His research interests span a wide range of topics in the representation and analysis of visual images, in both machine and biological systems. He is a fellow of the IEEE.