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.
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.
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.
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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Cortical enlightenment: are attentional gamma oscillations driven by ING or PING?
Paul Tiesinga;Terrence J. Sejnowski;Terrence J. Sejnowski.
Regulation of spike timing in visual cortical circuits
Paul Tiesinga;Jean Marc Fellous;Terrence J. Sejnowski;Terrence J. Sejnowski.
Nature Reviews Neuroscience (2008)
A computational psychiatry approach identifies how alpha-2A noradrenergic agonist Guanfacine affects feature-based reinforcement learning in the macaque.
S.A. Hassani;M. Oemisch;M. Balcarras;S. Westendorff.
Scientific Reports (2017)
A new correlation-based measure of spike timing reliability
Susanne Schreiber;Jean-Marc Fellous;D. Whitmer;Paul H. E. Tiesinga;Paul H. E. Tiesinga.
Oscillations in the prefrontal cortex: a gateway to memory and attention.
Karim Benchenane;Paul H Tiesinga;Paul H Tiesinga;Francesco P Battaglia.
Current Opinion in Neurobiology (2011)
Dynamic circuit motifs underlying rhythmic gain control, gating and integration.
Thilo Womelsdorf;Taufik A Valiante;Ned T Sahin;Kai J Miller.
Nature Neuroscience (2014)
Robust Gamma Coherence between Macaque V1 and V2 by Dynamic Frequency Matching
Mark J. Roberts;Mark J. Roberts;Eric Lowet;Eric Lowet;Nicolas M. Brunet;Nicolas M. Brunet;Marije Ter Wal.
Discovering spike patterns in neuronal responses
Jean Marc Fellous;Paul H.E. Tiesinga;Paul H.E. Tiesinga;Peter J. Thomas;Terrence J. Sejnowski.
The Journal of Neuroscience (2004)
Frequency dependence of spike timing reliability in cortical pyramidal cells and interneurons.
J.-M. Fellous;A. R. Houweling;R. H. Modi;R.P.N. Rao.
Journal of Neurophysiology (2001)
Computational model of carbachol-induced delta, theta, and gamma oscillations in the hippocampus.
Paul H.E. Tiesinga;Jean Marc Fellous;Jean Marc Fellous;Jorge V. José;Terrence J. Sejnowski.
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