Home

Active Research Projects

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.

Recent Publication

News

September 01, 2022

Welcome Paul Linton!

The lab welcomes new postdoctoral researcher Paul Linton to the Zuckerman Institute and the Presidential Scholars in Society and Neuroscience Program.

August 01, 2022

New Grant from NIH

The lab received a new grant from the National Institutes of Health BRAIN Initiative, “Revealing the mechanisms of primate face recognition with synthetic stimulus sets optimized to compare computational models”, in collaboration with Winrich Freiwald’s lab at Rockefeller University.

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.