What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Neuroscience
- Artificial neural network
His scientific interests lie mostly in Neuroscience, Artificial intelligence, Visual processing, Communication and Pattern recognition.
His Visual cortex, Stimulus and Sensory system study, which is part of a larger body of work in Neuroscience, is frequently linked to Population, bridging the gap between disciplines.
Artificial neural network is the focus of his Artificial intelligence research.
The various areas that he examines in his Visual processing study include Vigilance and Information processing.
His Information processing research is multidisciplinary, relying on both Speech recognition and Superordinate goals.
His Communication research includes themes of Visual perception, Categorization and Computer vision.
His most cited work include:
- Speed of processing in the human visual system. (2825 citations)
- The Orbitofrontal Cortex: Neuronal Activity in the Behaving Monkey (720 citations)
- Spike-based strategies for rapid processing. (586 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Neuroscience and Visual processing.
His Artificial intelligence study frequently involves adjacent topics like Neuron.
His Computer vision research focuses on Saccade and how it connects with Visual search.
His work on Visual cortex, Stimulus, Premovement neuronal activity and Caudate nucleus as part of general Neuroscience study is frequently connected to Population, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
The study incorporates disciplines such as Visual perception and Retina, Visual system in addition to Visual processing.
Simon J. Thorpe combines subjects such as Perception, Communication, Speech recognition, Information processing and Human visual system model with his study of Categorization.
He most often published in these fields:
- Artificial intelligence (48.07%)
- Computer vision (22.65%)
- Pattern recognition (21.55%)
What were the highlights of his more recent work (between 2015-2020)?
- Artificial intelligence (48.07%)
- Pattern recognition (21.55%)
- Speech recognition (22.65%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Pattern recognition, Speech recognition, Stimulus and Computer vision.
In most of his Artificial intelligence studies, his work intersects topics such as Neuron.
His Pattern recognition study incorporates themes from Artificial neural network, MNIST database and Spiking neural network.
His Speech recognition study also includes
- Electroencephalography that intertwine with fields like Response time, Natural, Early vision and Visual processing,
- Face which is related to area like Superordinate goals and Contrast.
His Stimulus research is multidisciplinary, incorporating perspectives in Sensory memory, Eye tracking and Audiology.
His biological study spans a wide range of topics, including Saccade and Visual hierarchy.
Between 2015 and 2020, his most popular works were:
- STDP-based spiking deep convolutional neural networks for object recognition (270 citations)
- Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks (43 citations)
- Finding faces, animals, and vehicles in far peripheral vision. (38 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Neuroscience
- Artificial neural network
Simon J. Thorpe spends much of his time researching Artificial intelligence, Pattern recognition, MNIST database, Artificial neural network and Spiking neural network.
Simon J. Thorpe combines topics linked to Computer vision with his work on Artificial intelligence.
He focuses mostly in the field of Pattern recognition, narrowing it down to topics relating to Cognitive neuroscience of visual object recognition and, in certain cases, Deep neural networks, Convolutional neural network, Deep learning and Neuron.
His research in MNIST database intersects with topics in Unsupervised learning, Feature learning, Winner-take-all and Learning rule.
His Spiking neural network study combines topics from a wide range of disciplines, such as Classifier, Backpropagation and Digit recognition.
His Saccade study combines topics in areas such as Stimulus, Visual processing, Human visual system model, Visual search and Visual field.
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