Rosa Cao: What and how can neural network models explain about the brain?

Colloquium lecture by Rosa Cao (Stanford), organized by the Department of Theoretical Philosophy

Neural networks now show impressive performance at increasingly interesting tasks, including perceptual tasks that are easy for humans but historically difficult for artificial systems. It has been widely claimed however, that these neural network models are in no way explanatory for neuroscience - either because they are too unlike the brain, or because they are themselves unintelligible. Less widely appreciated, however, are a series of recent results showing that neural network models trained to classify images are also surprisingly good at predicting neural activity. In fact, these models now provide our best predictions of visual neural response properties, despite never being trained on any brain data at all. I'll address two questions: Why are these models so successful at predicting brain activity? And does their predictive success tell us anything about how the brain itself works?

Rosa Cao is assistant professor at Stanford University. This is what she writes on her website: “ I’m interested in issues at the intersection of philosophy of mind, neuroscience, and cognitive science.  Can we get a naturalistic theory of representation that works for neuroscience? How should neural computation be understood, and what is the neural code?  How does physiology constrain functional architecture, and vice versa? ”