What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Neuroscience
- Statistics
Byron M. Yu focuses on Neuroscience, Premotor cortex, Stimulus, Electrophysiology and Nerve net.
His Neuroscience study incorporates themes from Control algorithm and Dimensionality reduction.
His Stimulus study frequently draws connections to adjacent fields such as Posterior parietal cortex.
His work in Electrophysiology tackles topics such as Motor cortex which are related to areas like Biological neural network.
His work deals with themes such as Neurophysiology, Sensory system, Primary motor cortex, Visualization and Rendering, which intersect with Nerve net.
His Neural variability research includes themes of Cerebral cortex, Wakefulness, Visual perception and CATS.
His most cited work include:
- Stimulus onset quenches neural variability: a widespread cortical phenomenon (761 citations)
- Stimulus onset quenches neural variability: a widespread cortical phenomenon (761 citations)
- Dimensionality reduction for large-scale neural recordings. (547 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Neuroscience, Artificial intelligence, Brain–computer interface, Pattern recognition and Neural activity.
In his study, he carries out multidisciplinary Neuroscience and Premotor cortex research.
Byron M. Yu combines subjects such as Machine learning, Neurophysiology, Decoding methods and Computer vision with his study of Artificial intelligence.
His research integrates issues of Kalman filter, Cursor and Motor control in his study of Brain–computer interface.
In his work, Poisson distribution is strongly intertwined with Estimator, which is a subfield of Pattern recognition.
His study in Stimulus is interdisciplinary in nature, drawing from both Cerebral cortex, Visual perception, Wakefulness and Posterior parietal cortex.
He most often published in these fields:
- Neuroscience (45.45%)
- Artificial intelligence (37.37%)
- Brain–computer interface (25.25%)
What were the highlights of his more recent work (between 2016-2021)?
- Neuroscience (45.45%)
- Neural activity (16.16%)
- Sensory system (9.09%)
In recent papers he was focusing on the following fields of study:
Neuroscience, Neural activity, Sensory system, Artificial intelligence and Stimulus are his primary areas of study.
Byron M. Yu regularly links together related areas like Dimensionality reduction in his Neuroscience studies.
He interconnects Cognitive science, Brain–computer interface and Set in the investigation of issues within Neural activity.
His Brain–computer interface research integrates issues from Cursor, Computer vision, User intent and Primary motor cortex.
His Sensory system study combines topics in areas such as Robotic arm, Human–computer interaction and Neural decoding.
Byron M. Yu works mostly in the field of Artificial intelligence, limiting it down to concerns involving Pattern recognition and, occasionally, Statistic and Redundancy.
Between 2016 and 2021, his most popular works were:
- A theory of multineuronal dimensionality, dynamics and measurement (107 citations)
- Learning by neural reassociation. (90 citations)
- Cortical Areas Interact through a Communication Subspace. (80 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Neuroscience
- Statistics
His main research concerns Neuroscience, Dimensionality reduction, Brain–computer interface, Neural activity and Motor cortex.
His studies in Neuroscience integrate themes in fields like Network model and Pairwise comparison.
Byron M. Yu combines topics linked to Curse of dimensionality with his work on Dimensionality reduction.
His Primary motor cortex research extends to Brain–computer interface, which is thematically connected.
Byron M. Yu focuses mostly in the field of Primary motor cortex, narrowing it down to topics relating to Pattern recognition and, in certain cases, Motor control.
His research in Motor control intersects with topics in Artificial neural network and Artificial intelligence.
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