James DiCarlo



  • Fellow
  • Learning in Machines & Brains


  • Massachusetts Institute of Technology
Department of Brain and Cognitive Sciences


  • United States


PhD (Biomedical Engineering), Johns Hopkins University
BSE (Biomedical Engineering), Northwestern University


The research goal of James DiCarlo’s group is a computational understanding of the brain mechanisms that underlie object recognition.

His group is currently focused on understanding how transformations carried out by a series of neocortical processing stages – called the primate ventral visual stream – are effortlessly able to untangle object identity from other latent image variables such as object position, scale and pose. DiCarlo and his collaborators have shown that populations of neurons at the highest cortical visual processing stage (IT) rapidly (<200 ms) convey explicit representations of object identity, even in the face of naturally occurring image variability. His group has found that the ventral stream’s ability to accomplish this feat is rapidly reshaped by natural visual experience, and they can now monitor the neuronal substrates of this learning online. This points the way to understanding how the visual system uses the statistics of the visual world to ‘learn’ neuronal representations that automatically untangle object identity.

DiCarlo and his collaborators have also shown how carefully designed visual recognition tests can be used to discover new, high-performing bio-inspired algorithms, and to efficiently explore the hypothesis space of possible cortical algorithms. His group is currently using a combination of large-scale neurophysiology, brain imaging, optogenetic methods and high-throughput computational simulations to understand the neuronal mechanisms and fundamental cortical computations that underlie the construction of these powerful image representations. They aim to use this understanding to inspire and develop new machine vision systems, to provide a basis for new neural prosthetics (brain-machine interfaces) to restore or augment lost senses, and to provide a foundation upon which the community can understand how high-level visual representation is altered in human conditions such as agnosia, autism and dyslexia.



McKnight Scholar Award in Neuroscience, McKnight Foundation, 2006–09

Surdna Research Foundation Award, MIT, 2005

MIT School of Science Prize for Excellence in Undergraduate Teaching, 2005

Pew Scholar in the Biomedical Sciences, 2002–06

Alfred P. Sloan Research Fellow, 2002

Relevant Publications

Li, N. et al. "What response properties do individual neurons need to underlie position and clutter ‘invariant’ object recognition?" J. Neurophysiol. 102, no. 1 (July 2009): 360–76.

DiCarlo, J.J., and N. Li. "Unsupervised natural experience rapidly alters invariant object representation in visual cortex." Science 321, no. 5895 (September 2008): 1502–1507.