Assistant Professor Daniel Yamins, Department of Psychology, Stanford University
Title: Four Not-So-Easy Pieces: Improving Neural Network Models of the Visual System
Abstract: Convolutional Neural Networks (CNNs) optimized for invariant category recognition have turned out to be OK, if imperfect, models of neural responses in the ventral visual pathway of humans and non-human primates. However, these very imperfections are the touchstones for a series of deep unsolved questions about neural architecture, visual cognition, and biological learning. In fact, every major component of the visual pathway-qua-task-optimized system is problematic in the standard CNN model: (1) from an architectural point of view, the lack of recurrent and feedback; (2) from a task objective point of view, the need for large numbers of supervision labels; (3) from an environment point of view, the reliance on offline batch learning of stereotyped image data; and (4) from a learning-rule perspective, the failures of back-propagation as a biologically realistic process. In this talk, I will discuss each of these problems in turn, together with initial forays toward their solution. Ultimately, I hope to communicate a sketch of what a more holistically plausible model of the ventral visual pathway might look like.