Chris Baker is Chief of the Section on Learning and Plasticity in the Laboratory of Brain and Cognition at NIMH. He received his PhD from the University of St. Andrews in Scotland (working with Dave Perrett) before conducting postdoctoral studies at Carnegie Mellon University (working with Carl Olson and Marlene Behrmann) and MIT (working with Nancy Kanwisher). One major research focus is on understanding how high-level visual information is represented in the brain, and how such representations are modified by experience. His lab uses multidisciplinary approaches to tackle these questions including neuroimaging (fMRI, MEG), brain stimulation (TMS) and behavioral approaches.
Deep neural networks can now achieve human-like levels of performance on tasks such as visual categorization, and are increasingly being viewed as a viable computational model for brain function. In this talk I will present recent work from my lab comparing deep neural networks with both behavioral and neuroimaging experiments (fMRI and MEG) investigating object and scene perception. While deep neural networks show a correspondence with both neuroimaging and behavioral data, our results reveal a complex relationship between the three domains. Given our findings, a key question is how can we move beyond establishing mere correspondences between models and brain data towards generating truly novel insight into the sensory representations underlying adaptive behavior.