Search results for "Coding Mechanisms"

showing 4 items of 4 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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Biologically inspired information processing and synchronization in ensembles of non-identical threshold-potential nanostructures.

2013

Nanotechnology produces basic structures that show a significant variability in their individual physical properties. This experimental fact may constitute a serious limitation for most applications requiring nominally identical building blocks. On the other hand, biological diversity is found in most natural systems. We show that reliable information processing can be achieved with heterogeneous groups of non-identical nanostructures by using some conceptual schemes characteristic of biological networks (diversity, frequency-based signal processing, rate and rank order coding, and synchronization). To this end, we simulate the integrated response of an ensemble of single-electron transisto…

Time FactorsTransistors ElectronicScienceMaterials ScienceMonte Carlo methodSynchronizationMaterial by AttributeSet (abstract data type)BiomimeticsImage Processing Computer-AssistedNanotechnologyBiologyNanomaterialsComputational NeurosciencePhysicsCoding MechanismsSignal processingMultidisciplinaryQInformation processingRComputational BiologySignal Processing Computer-AssistedSensory SystemsNanostructuresBionanotechnologyElectronic MaterialsProbability distributionMedicineBiological systemMonte Carlo MethodRealization (systems)Biological networkResearch ArticleBiotechnologyNeurosciencePLoS ONE
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Sparse Distributed Representation of Odors in a Large-scale Olfactory Bulb Circuit

2013

In the olfactory bulb, lateral inhibition mediated by granule cells has been suggested to modulate the timing of mitral cell firing, thereby shaping the representation of input odorants. Current experimental techniques, however, do not enable a clear study of how the mitral-granule cell network sculpts odor inputs to represent odor information spatially and temporally. To address this critical step in the neural basis of odor recognition, we built a biophysical network model of mitral and granule cells, corresponding to 1/100th of the real system in the rat, and used direct experimental imaging data of glomeruli activated by various odors. The model allows the systematic investigation and g…

Circuit ModelsMaleNerve net0302 clinical medicineLateral inhibitionOdorlcsh:QH301-705.5NeuronsFeedback PhysiologicalCoding Mechanisms0303 health sciencesNeuronal PlasticityEcologyAnatomyOlfactory BulbSynapseSensory Systemsmedicine.anatomical_structureComputational Theory and MathematicsModeling and SimulationExcitatory postsynaptic potentialResearch ArticleModels NeurologicalBiologyInhibitory postsynaptic potential03 medical and health sciencesCellular and Molecular NeuroscienceGeneticNeuroplasticityGeneticsmedicineAnimalsComputer SimulationBiologyMolecular BiologyEcology Evolution Behavior and Systematics030304 developmental biologyComputational NeuroscienceOlfactory SystemAnimalComputational BiologyNeuronEcology Evolution Behavior and SystematicRatsOlfactory bulbOdorlcsh:Biology (General)OdorantsSynapsesSynaptic plasticityRatNerve NetNeuroscience030217 neurology & neurosurgeryNeuroscience
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Spatio-Chromatic Adaptation via Higher-Order Canonical Correlation Analysis of Natural Images

2014

Independent component and canonical correlation analysis are two general-purpose statistical methods with wide applicability. In neuroscience, independent component analysis of chromatic natural images explains the spatio-chromatic structure of primary cortical receptive fields in terms of properties of the visual environment. Canonical correlation analysis explains similarly chromatic adaptation to different illuminations. But, as we show in this paper, neither of the two methods generalizes well to explain both spatio-chromatic processing and adaptation at the same time. We propose a statistical method which combines the desirable properties of independent component and canonical correlat…

LightVisual SystemRECEPTIVE-FIELD PROPERTIESlcsh:MedicineSocial and Behavioral SciencesBioinformaticsSTRIATE CORTEXCOLOR APPEARANCEImage Processing Computer-AssistedPsychophysicsPsychologylcsh:ScienceVisual CortexMathematicsCoding MechanismsMultidisciplinarySPECTRAL DESCRIPTIONSStatisticsSensory SystemsPRIMARY VISUAL-CORTEXDATA SETSPrincipal component analysisSensory PerceptionSPATIAL STRUCTURECanonical correlationAlgorithmsColor PerceptionResearch ArticleeducationColorCHROMATIC MECHANISMS114 Physical sciencesArtificial IntelligenceComponent (UML)PsychophysicsHumansComputer SimulationChromatic scaleStatistical MethodsBiologyProbabilityComputational NeuroscienceModels StatisticalINDEPENDENT COMPONENT ANALYSISbusiness.industrylcsh:RNeurosciencesComputational BiologyPattern recognitionIndependent component analysisData set2-STAGE LINEAR RECOVERYChromatic adaptationlcsh:QArtificial intelligencebusinessPhotic StimulationMathematicsNeurosciencePLoS ONE
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