COURSE DESCRIPTION
In this short course, we will study how visual representation in a variety of artificial neural networks (ANNs) is similar to and different from that in the visual cortex. We will talk about how to adapt approaches from visual neuroscience to carry out experiments in ANNs that can provide insight into internal visual representation, and we will discuss how insights from ANNs can then influence how we conduct visual neurophysiology experiments. We will carry out hands-on experiments in python using pytorch.

TARGET AUDIENCE
Students at any level who are interested in visual representation in neuroscience or artificial networks. Basic knowledge of Python will be useful.


PROGRAM
August 20-22 from 13:30 to 15:30 in room B715 Grad School

Sign up at https://forms.office.com/r/ZrwJrNN69y 


BIOGRAPHY
I received my bachelor's degree in Computer Science at Penn State University and my doctorate in Computation and Neural Systems from Caltech, where my PhD work focused on computer modeling of the visual cortex, neural coding and analog VLSI circuits.  As a post-doc at New York University, I studied temporal dynamics in cortical motion and form processing via multi-electrode recordings in non-human primates (NHPs). I started my laboratory at the University of Oxford, UK, funded by the Wellcome Trust and supported by St. John's College, where I studied neural coding in the visual cortex and began to build a general modeling framework for the motion processing pathway from retina to cortical area MT. In 2011, I moved to the University of Washington in Seattle, WA, USA, where I now have a 2- and 3-photon imaging rig for NHPs at the Washington National Primate Research Center (WaNPRC). I have expanded my areas of interest to include mid-level form processing and "Artiphysiology" - the study of artificial neural networks as model visual systems.  In collaboration with the Allen Institute for Brain Sciences, my lab is advancing functional connectomic studies of the primate visual cortex.