His primary scientific interests are in Artificial intelligence, Visual cortex, Pattern recognition, Self-organizing map and Neuroscience. His Artificial intelligence research includes themes of Machine learning and Computer vision. Within one scientific family, Klaus Obermayer focuses on topics pertaining to Receptive field under Visual cortex, and may sometimes address concerns connected to Cortex, Macaque and Visual space.
The study incorporates disciplines such as Orientation, Feature and Matched filter in addition to Pattern recognition. His Self-organizing map research incorporates elements of Vector quantization, Statistical physics, Function, Rate of convergence and Unit interval. The concepts of his Neuroscience study are interwoven with issues in Biological system and Computation.
Klaus Obermayer mostly deals with Artificial intelligence, Neuroscience, Pattern recognition, Visual cortex and Computer vision. The Artificial intelligence study combines topics in areas such as Algorithm and Machine learning. His Algorithm study frequently draws connections between related disciplines such as Cluster analysis.
His study involves Excitatory postsynaptic potential, Inhibitory postsynaptic potential, Macaque, Striate cortex and Neuron, a branch of Neuroscience. Feature vector is the focus of his Pattern recognition research. His Visual cortex research incorporates themes from Stimulus, Network model and Receptive field.
Neuroscience, Artificial intelligence, Neuron, Stimulus and Pattern recognition are his primary areas of study. Neuroscience and Synaptic cleft are commonly linked in his work. Artificial intelligence connects with themes related to Computer vision in his study.
The various areas that Klaus Obermayer examines in his Stimulus study include Expectancy theory, Weighting, Visual cortex and Amygdala. His research in Visual cortex intersects with topics in Visual processing, Behavioral choice and Bursting. His studies examine the connections between Pattern recognition and genetics, as well as such issues in Macaque, with regards to Decoding methods and Coding.
His main research concerns Artificial intelligence, Neuroscience, Artificial neural network, Neuron and Biological system. He combines subjects such as State, Computer vision and Pattern recognition with his study of Artificial intelligence. His work in the fields of Neuroscience, such as Prefrontal cortex and Endophenotype, overlaps with other areas such as Alcohol dependence and Ventral striatum.
His studies deal with areas such as Pairwise comparison, Pseudolikelihood and Neural decoding as well as Artificial neural network. His Neuron study incorporates themes from Network model, Synaptic cleft, Inhibitory postsynaptic potential, Computational model and Tripartite synapse. Klaus Obermayer has researched Biological system in several fields, including Bayes estimator and Covariance.
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Support vector learning for ordinal regression
R. Herbrich;T. Graepel;K. Obermayer.
international conference on artificial neural networks (1999)
Self-organizing maps: ordering, convergence properties and energy functions
E. Erwin;K. Obermayer;K. Schulten.
Biological Cybernetics (1992)
Geometry of orientation and ocular dominance columns in monkey striate cortex
K Obermayer;GG Blasdel.
The Journal of Neuroscience (1993)
Invariant computations in local cortical networks with balanced excitation and inhibition
Jorge Mariño;James Schummers;David C Lyon;David C Lyon;Lars Schwabe.
Nature Neuroscience (2005)
Models of Orientation and Ocular Dominance Columns in the Visual Cortex: A Critical Comparison
E. Erwin;Klaus Obermayer;Klaus Schulten.
Neural Computation (1995)
A new summarization method for affymetrix probe level data
Sepp Hochreiter;Djork-Arné Clevert;Klaus Obermayer.
A principle for the formation of the spatial structure of cortical feature maps.
Klaus Obermayer;Helge Ritter;Klaus Schulten.
Proceedings of the National Academy of Sciences of the United States of America (1990)
Statistical-mechanical analysis of self-organization and pattern formation during the development of visual maps
K. Obermayer;G. G. Blasdel;K. Schulten.
Physical Review A (1992)
Soft learning vector quantization
Sambu Seo;Klaus Obermayer.
Neural Computation (2003)
New methods for the computer-assisted 3-D reconstruction of neurons from confocal image stacks.
Stephan Schmitt;Jan Felix Evers;Carsten Duch;Michael Scholz.
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