James DiCarlo

Director, MIT Quest for Intelligence

Co-Director, Center for Brains, Minds, and Machines

Peter de Florez Professor, Department of Brain and Cognitive Sciences

McGovern Institute for Brain Research

Computational Neuroscience

Machine Learning

Computer Vision

External links

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Missions

Embodied Intelligence

Projects

MIT Covid-19 Response System (MCRS)

Synopsis

James DiCarlo is interested in understanding how a complex network of brain regions underlies our ability to recognize vast numbers of objects and faces rapidly.

Biography

James DiCarlo is the Peter de Florez Professor of Neuroscience in the Department of Brain and Cognitive Science at MIT and director of the MIT Quest for Intelligence, and is a principal investigator at the McGovern Institute for Brain Research. His research focuses on using computational methods to understand the brain’s visual system, and with this knowledge, developing brain-machine interfaces to restore or augment lost senses. DiCarlo has received an Alfred P. Sloan fellowship, a Pew Scholar Award, and a McKnight Scholar Award. He earned a PhD in biomedical engineering, and an MD, from Johns Hopkins University.

James DiCarlo

Synopsis

Biography

Publications

An empirical assay of view-invariant object learning in humans and comparison with baseline image-computable models

Adversarially trained neural representations may already be as robust as corresponding biological neural representations

Evidence that recurrent circuits are critical to the ventral stream's execution of core object recognition behavior.

An empirical assay of view-invariant object learning in humans and comparison with baseline image-computable models

Adversarially trained neural representations may already be as robust as corresponding biological neural representations

Evidence that recurrent circuits are critical to the ventral stream's execution of core object recognition behavior.

Unsupervised changes in core object recognition behavioral performance are accurately predicted by unsupervised neural plasticity in inferior temporal cortex

Neural population control via deep image synthesis.

In the News

Putting Vision Models to the Test

How the brain distinguishes between objects

Smarter AIs could help us understand how our brains interpret the world

Putting Vision Models to the Test

How the brain distinguishes between objects

Smarter AIs could help us understand how our brains interpret the world

To Advance Artificial Intelligence, Reverse-Engineer the Brain.

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