2024
Population encoding of stimulus features along the visual hierarchy
Dyballa L, Rudzite A, Hoseini M, Thapa M, Stryker M, Field G, Zucker S. Population encoding of stimulus features along the visual hierarchy. Proceedings Of The National Academy Of Sciences Of The United States Of America 2024, 121: e2317773121. PMID: 38227668, PMCID: PMC10823231, DOI: 10.1073/pnas.2317773121.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsMiceNeural Networks, ComputerNeuronsPhotic StimulationRetinaVisual CortexVisual PerceptionConceptsPartitioning feature spaceConvolutional neural networkNeural populationsDiverse visual featuresBattery of visual stimuliManifold embedding techniquesMachine learning approachFeature spaceEncoded featuresVisual featuresNeural networkProperties of individual neuronsPrimary visual cortexPopulation encodingEmbedding techniqueLearning approachPartitioning featuresVisual hierarchyContinuous representationV1 populationsStimulus spaceMulti-electrode arrayGroups of neuronsIndividual neuronsVisual cortex
2005
Geometric diffusions for the analysis of data from sensor networks
Coifman RR, Maggioni M, Zucker SW, Kevrekidis IG. Geometric diffusions for the analysis of data from sensor networks. Current Opinion In Neurobiology 2005, 15: 576-584. PMID: 16150587, DOI: 10.1016/j.conb.2005.08.012.Peer-Reviewed Original ResearchConceptsSensor networksGeometric diffusionMathematical developmentComplex data setsHarmonic analysisNeural information processingActivity datasetsCertain analogyComputer modelingData setsInformation processingManifoldNetworkModelingGraphData analysisAlgorithmNew toolDatasetAnalysis of dataAnalogyField