World's Best Scientists 2026 revealed!
James J. DiCarlo

James J. DiCarlo

D-Index & Metrics

Neuroscience

D-Index
68
Citations
45434
World Ranking
2691
National Ranking
1261

Research.com Recognitions

  • 2002 - Fellow of Alfred P. Sloan Foundation

Overview

James J. DiCarlo is affiliated with MIT in the United States and has a research focus primarily in neuroscience and computer science. Their work spans several subfields including cognitive neuroscience, computer vision and pattern recognition, artificial intelligence, electrical and electronic engineering, and biophysics.

The scientist's research topics include:

  • Neural dynamics and brain function
  • Face recognition and perception
  • Visual perception and processing mechanisms
  • Visual attention and saliency detection
  • Cell image analysis techniques
  • Advanced memory and neural computing
  • Memory and neural mechanisms

Notable recent publications by James J. DiCarlo are:

  • Unsupervised neural network models of the ventral visual stream, 2021, Proceedings of the National Academy of Sciences
  • Catalyzing next-generation Artificial Intelligence through NeuroAI, 2023, Nature Communications
  • Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence, 2020, Neuron
  • Fast Recurrent Processing via Ventrolateral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition, 2020, Neuron
  • Next-generation deep learning based on simulators and synthetic data, 2021, Trends in Cognitive Sciences

Frequent coauthors include:

  • Daniel Yamins
  • Kohitij Kar
  • Martin Schrimpf
  • Joel Dapello
  • Tiago Marques

The scientist has published extensively in several venues, with the most frequent being:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • arXiv (Cornell University)
  • Journal of Vision
  • Neuron
  • Nature Communications

James J. DiCarlo was recognized as a Fellow of the Alfred P. Sloan Foundation in 2002.

Best Publications

  • How Does the Brain Solve Visual Object Recognition

    James J. DiCarlo;Davide Zoccolan;Nicole C. Rust

  • Performance-optimized hierarchical models predict neural responses in higher visual cortex

    Daniel L. K. Yamins;Ha Hong;Charles Cadieu;Ethan A. Solomon

  • Using goal-driven deep learning models to understand sensory cortex

    Daniel L K Yamins;James J DiCarlo;James J DiCarlo

  • Untangling invariant object recognition.

    James J. DiCarlo;David D. Cox

  • Fast Readout of Object Identity from Macaque Inferior Temporal Cortex

    Chou P. Hung;Chou P. Hung;Gabriel Kreiman;Tomaso Poggio;James J. DiCarlo;James J. DiCarlo

  • Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition

    Charles F. Cadieu;Ha Hong;Daniel L. K. Yamins;Nicolas Pinto

  • Why is Real-World Visual Object Recognition Hard?

    Nicolas Pinto;David Daniel Cox;David Daniel Cox;David Daniel Cox;James J DiCarlo;James J DiCarlo

  • Design of a synthetic yeast genome

    Sarah M. Richardson;Sarah M. Richardson;Leslie A. Mitchell;Leslie A. Mitchell;Giovanni Stracquadanio;Giovanni Stracquadanio;Giovanni Stracquadanio;Kun Yang;Kun Yang

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

    Kohitij Kar;Jonas Kubilius;Jonas Kubilius;Kailyn Schmidt;Elias B Issa;Elias B Issa

  • Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?

    Martin Schrimpf;Jonas Kubilius;Jonas Kubilius;Ha Hong;Najib Majaj

  • Neural population control via deep image synthesis.

    Pouya Bashivan;Kohitij Kar;James J. DiCarlo

  • Selectivity and tolerance ("invariance") both increase as visual information propagates from cortical area V4 to IT.

    Nicole C. Rust;James J. DiCarlo

  • Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks

    Rishi Rajalingham;Elias B. Issa;Pouya Bashivan;Kohitij Kar

  • Object Selectivity of Local Field Potentials and Spikes in the Macaque Inferior Temporal Cortex

    Gabriel Kreiman;Chou P. Hung;Chou P. Hung;Alexander Kraskov;Rodrigo Quian Quiroga

  • A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation

    Nicolas Pinto;Nicolas Pinto;David Doukhan;David Doukhan;James J. DiCarlo;James J. DiCarlo;David Daniel Cox;David Daniel Cox;David Daniel Cox

  • Explicit information for category-orthogonal object properties increases along the ventral stream

    Ha Hong;Daniel L K Yamins;Daniel L K Yamins;Najib J Majaj;Najib J Majaj;Najib J Majaj;James J DiCarlo;James J DiCarlo

  • Unsupervised neural network models of the ventral visual stream

    Chengxu Zhuang;Siming Yan;Aran Nayebi;Martin Schrimpf

  • Stimulus configuration, classical conditioning, and hippocampal function.

    Nestor A. Schmajuk;James J. DiCarlo

  • Unsupervised Natural Experience Rapidly Alters Invariant Object Representation in Visual Cortex

    Nuo Li;James J. DiCarlo

  • Discrimination training alters object representations in human extrastriate cortex.

    Hans P. Op de Beeck;Chris I. Baker;James J. DiCarlo;Nancy G. Kanwisher

  • How far can you get with a modern face recognition test set using only simple features

    Nicolas Pinto;James J DiCarlo;David D Cox

Frequent Co-Authors

Kenneth O. Johnson
Kenneth O. Johnson Johns Hopkins University
Stanislas Dehaene
Stanislas Dehaene Collège de France
Gabriel Kreiman
Gabriel Kreiman Harvard University
Hans Op de Beeck
Hans Op de Beeck Allen Institute for Brain Science
John H. R. Maunsell
John H. R. Maunsell University of Chicago
Wim Vanduffel
Wim Vanduffel Harvard University
Steven S. Hsiao
Steven S. Hsiao Johns Hopkins University
Chuang Gan
Chuang Gan University of Massachusetts Amherst

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