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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

November 30, 2020

New Publication in PNAS

A new paper by postdoc Tal Golan, "Controversial stimuli: Pitting neural networks against each other as models of human cognition", is now out in PNAS (preprint version available at arXiv). Learn more about it here.

August 17, 2020

Welcome Wenxuan Guo!

The lab welcomes new graduate student Wenxuan Guo, who joins us through the Psychology program.

July 15, 2020

New Grant from NSF

The lab received a new grant from the National Science Foundation, "Testing the limits of deep neural network models of human vision with optimized stimuli."