What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Neuroscience
- Machine learning
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.
His most cited work include:
- How Does the Brain Solve Visual Object Recognition (1000 citations)
- Performance-optimized hierarchical models predict neural responses in higher visual cortex (960 citations)
- Using goal-driven deep learning models to understand sensory cortex (736 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (77.71%)
- Cognitive neuroscience of visual object recognition (54.82%)
- Pattern recognition (45.18%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (77.71%)
- Artificial neural network (34.94%)
- Pattern recognition (45.18%)
In recent papers he was focusing on the following fields of 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
- Visual processing that intertwine with fields like Human brain,
- Neuron and related Surround suppression.
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.
Between 2017 and 2021, his most popular works were:
- Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks (173 citations)
- Evidence that recurrent circuits are critical to the ventral stream's execution of core object recognition behavior. (140 citations)
- Evidence that recurrent circuits are critical to the ventral stream's execution of core object recognition behavior. (140 citations)
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