What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Neuroscience
- Neuron
His main research concerns Neuroscience, Neuron, Inhibitory postsynaptic potential, Prefrontal cortex and Stimulus.
His work in the fields of Neuroscience, such as Hippocampal formation, Visual cortex and Nerve net, intersects with other areas such as Spike train and Reliability.
His work deals with themes such as Memory consolidation, Electroencephalography, Working memory, Biological neural network and Dopamine, which intersect with Hippocampal formation.
In general Inhibitory postsynaptic potential study, his work on Excitatory postsynaptic potential often relates to the realm of Short interval, thereby connecting several areas of interest.
Paul H. E. Tiesinga has researched Prefrontal cortex in several fields, including Lateral geniculate nucleus, Surrogate data, Temporal lobe, Patch clamp and Brain mapping.
The Stimulus study combines topics in areas such as Contrast, Brain activity and meditation, Photic Stimulation and Pattern recognition.
His most cited work include:
- Cortical enlightenment: are attentional gamma oscillations driven by ING or PING? (308 citations)
- Regulation of spike timing in visual cortical circuits (292 citations)
- A new correlation-based measure of spike timing reliability (285 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Neuroscience, Neuron, Inhibitory postsynaptic potential, Stimulus and Excitatory postsynaptic potential.
His work is connected to Visual cortex, Interneuron, Receptive field, Local field potential and Sensory system, as a part of Neuroscience.
In Neuron, Paul H. E. Tiesinga works on issues like Neurotransmission, which are connected to Nerve net.
The various areas that Paul H. E. Tiesinga examines in his Inhibitory postsynaptic potential study include Biological neural network, Synaptic noise and Cluster.
His Stimulus study combines topics from a wide range of disciplines, such as Neural coding and Macaque.
His study in Excitatory postsynaptic potential is interdisciplinary in nature, drawing from both Carbachol, Hippocampus, Postsynaptic potential and Attentional modulation.
He most often published in these fields:
- Neuroscience (60.57%)
- Neuron (29.71%)
- Inhibitory postsynaptic potential (22.29%)
What were the highlights of his more recent work (between 2018-2021)?
- Neuroscience (60.57%)
- Artificial intelligence (12.57%)
- Electrode (2.29%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Neuroscience, Artificial intelligence, Electrode, Electroencephalography and Stimulus.
His work on Neuroscience is being expanded to include thematically relevant topics such as Adaptation.
His work carried out in the field of Artificial intelligence brings together such families of science as Stimulus control and Pattern recognition.
As a part of the same scientific study, Paul H. E. Tiesinga usually deals with the Electroencephalography, concentrating on Source separation and frequently concerns with Feature.
His Stimulus research incorporates elements of Artificial neural network, Multiple time dimensions, Cortical neuron, Dorsolateral prefrontal cortex and Surprise.
His research investigates the connection with Beta Rhythm and areas like Local field potential which intersect with concerns in Neuron.
Between 2018 and 2021, his most popular works were:
- Where is Cingulate Cortex? A Cross-Species View (28 citations)
- Feature-specific prediction errors and surprise across macaque fronto-striatal circuits (23 citations)
- Why not record from every electrode with a CMOS scanning probe (12 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Neuroscience
- Neuron
Neuroscience, Adaptation, Cingulate cortex, Time–frequency analysis and Artifact are his primary areas of study.
Much of his study explores Neuroscience relationship to Multiple time dimensions.
His Time–frequency analysis research encompasses a variety of disciplines, including Biological system, Bayesian probability, Local field potential, Synthetic data and Phase.
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