2002 - Fellow of Alfred P. Sloan Foundation
James J. DiCarlo focuses on Artificial intelligence, Cognitive neuroscience of visual object recognition, Neuroscience, Temporal cortex and Artificial neural network. His Artificial intelligence study typically links adjacent topics like Pattern recognition. The various areas that he examines in his Cognitive neuroscience of visual object recognition study include Visual perception, Visual cortex, Computational model and Variation.
In general Neuroscience study, his work on Visual system and Hippocampal formation often relates to the realm of Temporal lobe, thereby connecting several areas of interest. James J. DiCarlo has researched Temporal cortex in several fields, including Brain mapping and Macaque. The Artificial neural network study combines topics in areas such as Computational neuroscience and Stimulus.
James J. DiCarlo mainly investigates Artificial intelligence, Cognitive neuroscience of visual object recognition, Pattern recognition, Neuroscience and Artificial neural network. James J. DiCarlo combines subjects such as Temporal cortex, Visual cortex and Communication with his study of Artificial intelligence. His work carried out in the field of Cognitive neuroscience of visual object recognition brings together such families of science as Visual processing, Primate, Visual perception, Form perception and Machine learning.
His Pattern recognition study combines topics from a wide range of disciplines, such as Object, Functional magnetic resonance imaging, Face and Representation. His work on Macaque, Cortex and Neuron as part of general Neuroscience study is frequently connected to Temporal lobe, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. James J. DiCarlo has included themes like Speech recognition, Evolution of color vision in primates, Categorization and Benchmark in his Artificial neural network study.
His main research concerns Artificial intelligence, Artificial neural network, Pattern recognition, Cognitive neuroscience of visual object recognition and Neuroscience. His Artificial intelligence study frequently links to adjacent areas such as Machine learning. His Artificial neural network study also includes
His research integrates issues of Temporal cortex, Face and Visual cortex in his study of Pattern recognition. His Cognitive neuroscience of visual object recognition research is multidisciplinary, relying on both Evolution of color vision in primates and Benchmark. Macaque and Primate are the primary areas of interest in his Neuroscience study.
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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.
Proceedings of the National Academy of Sciences of the United States of America (2014)
Untangling invariant object recognition.
James J. DiCarlo;David D. Cox.
Trends in Cognitive Sciences (2007)
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.
Using goal-driven deep learning models to understand sensory cortex
Daniel L K Yamins;Daniel L K Yamins;James J DiCarlo;James J DiCarlo.
Nature Neuroscience (2016)
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.
PLOS Computational Biology (2008)
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.
PLOS Computational Biology (2014)
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.
PLOS Computational Biology (2009)
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.
Stimulus configuration, classical conditioning, and hippocampal function.
Nestor A. Schmajuk;James J. DiCarlo.
Psychological Review (1992)
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