What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Neuroscience
- Machine learning
Andreas S. Tolias mostly deals with Neuroscience, Wakefulness, Visual cortex, Macaque and Sensory system.
Electrophysiology, Neocortex, Cerebral cortex, Cortex and Functional connectivity are the core of his Neuroscience study.
He has researched Cerebral cortex in several fields, including Acetylcholine, Cholinergic and Optics.
His biological study spans a wide range of topics, including Nerve net, Photic Stimulation and Pupil.
His work carried out in the field of Visual cortex brings together such families of science as Stimulus, Form perception, Cortex and Pattern recognition.
His study looks at the relationship between Sensory system and topics such as Pupillometry, which overlap with Whisking in animals, Electroencephalography, Arousal and Motor activity.
His most cited work include:
- Decorrelated neuronal firing in cortical microcircuits. (532 citations)
- Principles of connectivity among morphologically defined cell types in adult neocortex (499 citations)
- Spike sorting for large, dense electrode arrays (405 citations)
What are the main themes of his work throughout his whole career to date?
Andreas S. Tolias mainly investigates Neuroscience, Visual cortex, Artificial intelligence, Macaque and Pattern recognition.
In his work, Andreas S. Tolias performs multidisciplinary research in Neuroscience and Cell type.
His work in Visual cortex covers topics such as Stimulus which are related to areas like Correlation.
His Artificial intelligence research incorporates themes from Machine learning and Spike.
His work in the fields of Pattern recognition, such as Convolutional neural network, intersects with other areas such as Generalization.
His Inference study deals with Calcium imaging intersecting with Biological neural network.
He most often published in these fields:
- Neuroscience (67.82%)
- Visual cortex (42.15%)
- Artificial intelligence (28.74%)
What were the highlights of his more recent work (between 2018-2021)?
- Neuroscience (67.82%)
- Artificial intelligence (28.74%)
- Visual cortex (42.15%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Neuroscience, Artificial intelligence, Visual cortex, Cell type and Neocortex.
The concepts of his Neuroscience study are interwoven with issues in Likelihood function and Bayesian probability.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Pattern recognition.
His Visual cortex research is multidisciplinary, incorporating elements of Normalization, Visual processing, Invariant and Neuron.
His research investigates the connection between Neocortex and topics such as Excitatory postsynaptic potential that intersect with issues in Patch clamp.
The Inhibitory postsynaptic potential study combines topics in areas such as Somatosensory system, Electrophysiology and Cortex.
Between 2018 and 2021, his most popular works were:
- Three scenarios for continual learning (111 citations)
- Deep convolutional models improve predictions of macaque V1 responses to natural images. (100 citations)
- Engineering a Less Artificial Intelligence. (51 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Neuroscience
- Machine learning
His primary scientific interests are in Neuroscience, Visual cortex, Artificial intelligence, Cell type and Artificial neural network.
His Neuroscience study often links to related topics such as Probabilistic logic.
He combines subjects such as Visual processing, Sensory system, Behavioral state, Sensorimotor control and Brain circuitry with his study of Visual cortex.
His study in Sensory system is interdisciplinary in nature, drawing from both Perception, Categorization, Bayesian probability, Neural coding and Likelihood function.
His work investigates the relationship between Artificial intelligence and topics such as Pattern recognition that intersect with problems in Feature, Neuronal tuning and Transfer of learning.
His work on Learning rule as part of general Artificial neural network research is often related to Network architecture, thus linking different fields of science.
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