Spring 2025 GRASP on Robotics: Bruno Olshausen, University of California, Berkeley & Redwood Center for Theoretical Neuroscience, “Invariance and equivariance in brains and machine”
February 28 @ 10:30 am - 11:45 am
This will be a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom.
ABSTRACT
The goal of building machines that can perceive and act in the world as humans and other animals do has been a focus of AI research efforts for over half a century. Over this same period, neuroscience has sought to achieve a mechanistic understanding of the brain processes underlying perception and action. It stands to reason that these parallel efforts could inform one another. Here I propose an approach to the long-standing problem invariant and equivariant representation in vision – that is, how do we recognize objects independent of pose, lighting and other variations, and how do we perceive such variations independent of object shape? The approach is rooted in observations of animal behavior and informed by both neurobiological mechanisms (recurrence, dendritic nonlinearities, phase coding) and mathematical principles (group theory, residue numbers). What emerges from this approach is a neural circuit for factorization that can learn about shapes and their transformations from image data, and a model of the grid-cell system based on high-dimensional encodings of residue numbers. These models provide efficient solutions to long-studied problems that are well-suited for implementation in neuromorphic hardware or as a basis for forming hypotheses about visual cortex and entorhinal cortex.
Presenter
Bruno Olshausen - Learn More
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.