Search results for "Retinotopy"

showing 2 items of 2 documents

Topographic Independent Component Analysis reveals random scrambling of orientation in visual space

2017

Neurons at primary visual cortex (V1) in humans and other species are edge filters organized in orientation maps. In these maps, neurons with similar orientation preference are clustered together in iso-orientation domains. These maps have two fundamental properties: (1) retinotopy, i.e. correspondence between displacements at the image space and displacements at the cortical surface, and (2) a trade-off between good coverage of the visual field with all orientations and continuity of iso-orientation domains in the cortical space. There is an active debate on the origin of these locally continuous maps. While most of the existing descriptions take purely geometric/mechanistic approaches whi…

0301 basic medicineComputer scienceVisionVisual spaceStatistics as Topiclcsh:MedicineSocial SciencesSpace (mathematics)Scramblingchemistry.chemical_compound0302 clinical medicineCognitionLearning and MemoryAnimal CellsMedicine and Health SciencesPsychologylcsh:Sciencemedia_commonVisual CortexNeuronsMammalsObject RecognitionCoding MechanismsBrain MappingMultidisciplinaryGeographyOrientation (computer vision)Visual fieldmedicine.anatomical_structureVertebratesSensory PerceptionCellular TypesAnatomyNeuronal TuningResearch ArticleCartographyPrimatesmedia_common.quotation_subjectOcular AnatomyRetina03 medical and health sciencesTopographic MapsOcular SystemMemoryPerceptionOrientationNeuronal tuningmedicineAnimalsHumansCortical surfaceComputational NeuroscienceRetinabusiness.industrylcsh:ROrganismsCognitive PsychologyBiology and Life SciencesComputational BiologyRetinalPattern recognitionCell Biology030104 developmental biologyVisual cortexchemistryRetinotopyCellular NeuroscienceAmniotesEarth SciencesCognitive Sciencelcsh:QPerceptionArtificial intelligencebusiness030217 neurology & neurosurgeryNeurosciencePLoS ONE
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Characteristics of neuronal systems in the visual cortex

1987

The coupling complexity of cortical areas makes it very difficult to analyse them experimentally. Studies of model systems provide the possibility of adapting the analysis to the available data base and elaborating the fundamental properties that depend on the structure of the system. We propose a model system of variable complexity that is spatially two-dimensional and time-dependent, uses feedback for iteration and smoothing, includes the mapping of the cortical networks and can be nonlinear as the case requires. Combining such elementary systems on the basis of neuroanatomical findings enables us to simulate cortical mappings and to interpret neurophysiological data. The decisive factor …

General Computer ScienceComputer scienceModels NeurologicalComplex systemRetinamedicineAnimalsVision OcularVisual CortexNeuronsQuantitative Biology::Neurons and CognitionBasis (linear algebra)business.industryPattern recognitionNeurophysiologyNonlinear systemVisual cortexmedicine.anatomical_structureCoupling (computer programming)RetinotopyVisual PerceptionArtificial intelligencebusinessMathematicsSmoothingBiotechnologyBiological Cybernetics
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