Abstract: Where does common sense come from? Is it learned or innate? Here I shall argue that while much of the factual knowledge that underlies our common sense is undoubtedly learned, the human capacity to do this is largely innate and inherited from our ancestors who inhabited the planet long before humans or even mammals arrived on the scene. What we call ‘common sense’ is actually required by myriad organisms across the animal kingdom in order to navigate, hunt, mate and survive in an ever changing physical environment. It is the ability to generalize and extract the rules by which the world works, and to apply those rules in novel situations or contexts. It requires specific mathematical and computational primitives - for example, the ability to compute geometric transformations - that go beyond the generic Perceptron-style computations that form the basis of most neural network models. I shall draw upon behavioral studies of jumping spiders, neuroanatomical observations, and computational models for disentangling form and motion to make these arguments.
Speaker bio: Bruno OIshausen is Professor of Neuroscience and Optometry at the University of California, Berkeley. He also serves as Director of the Redwood Center for Theoretical Neuroscience, an interdisciplinary research group focusing on mathematical and computational models of brain function. He received B.S. and M.S. degrees in Electrical Engineering from Stanford University, and a Ph.D. in Computation and Neural Systems from the California Institute of Technology. Prior to Berkeley he was a member of the Departments of Psychology and Neurobiology, Physiology & Behavior at UC Davis. During postdoctoral work with David Field at Cornell he developed the sparse coding model of visual cortex which provides a linking principle between natural scene statistics and the response properties of visual neurons. Olshausen's current research aims to understand the information processing strategies employed by the brain for doing tasks such as object recognition and scene analysis. This work seeks not only to advance our understanding of the brain, but also to discover new algorithms for scene analysis based on how brains work.