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Neural network modeling of visual perception and cognition

Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. We seek to develop neural network models that can meet real-world computational challenges faced by biological visual systems and that can also explain detailed patterns of brain and behavioral responses.

Development of statistical inference and visualization methods

Computational neuroscience is entering a new era, where big models meet big data. We develop statistical inference and visualization techniques that help us connect theory and experiment, enabling us, for example, to adjudicate among many candidate neural network models using brain-activity measurements acquired with functional imaging and electrophysiological recordings in animals and humans.

News

July 07, 2022

New Publication in PNAS

A new paper "Face dissimilarity judgements are predicted by representational distance in morphable and image-computable models" is now out in PNAS (preprint version available at bioRxiv), led by lab alumni Kamila Jozwik, Jonathan O'Keeffe, and Kate Storrs. Learn more about it here.

April 29, 2022

New Lab Fellowship

Congratulations to graduate student Wenxuan Guo for receiving a named fellowship from the Graduate School of Arts and Sciences! Wenxuan's fellowship comes from the Kamel S. Bahary Fellowship Fund.

April 29, 2022

New Lab Fellowship

Congratulations to postdoc JohnMark Taylor for receiving an NIH NRSA fellowship! JohnMark received the fellowship for his project "Fast and Flexible Conjunction Coding in Biological and Artificial Vision."