The improvements in AI in recent years have been nothing but astonishing. One important stepping stone for achieving this success was the recognition of the power of deep neural networks, which consist of repeated, interconnected layers. The brain has always operated this way, and much can be learned from how neural networks in the brain function. We are particularly interested in how information is transformed between different network stages, such as across different areas, or layers within an area. We investigate this question in the visual cortex as one of the best examples of a deep network. Our experiments use a number of cutting edge approaches, including massively parallel extracellular recordings from large populations of neurons in multiple visual areas, and two-photon calcium imaging to study large groups of neurons in the same area. We apply these techniques to study processing in normal brains, but also to investigate how visual networks evolve during development, and how they respond to injury. Results from our network-level neural studies are compared with computational deep networks to inform both the study of the real brain, but also to improve artificial vision.
The lab is part of the Solomon Snyder Department of Neuroscience. The lab is open to graduate students from the Neuroscience department, but also for students from Biomedical Engineering.