A mixture of sparse coding models explaining properties of face neurons related to holistic and parts-based processing

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dc.contributor.author Hosoya, Haruo
dc.contributor.author Hyvärinen, Aapo
dc.date.accessioned 2017-09-01T09:24:02Z
dc.date.available 2017-09-01T09:24:02Z
dc.date.issued 2017-07
dc.identifier.citation Hosoya , H & Hyvärinen , A 2017 , ' A mixture of sparse coding models explaining properties of face neurons related to holistic and parts-based processing ' , PLoS Computational Biology , vol. 13 , no. 7 , 1005667 . https://doi.org/10.1371/journal.pcbi.1005667
dc.identifier.other PURE: 89084679
dc.identifier.other PURE UUID: 33bc78cf-8273-4386-b54d-54062b5aa5cc
dc.identifier.other WOS: 000406619800044
dc.identifier.other Scopus: 85026681092
dc.identifier.other ORCID: /0000-0002-5806-4432/work/39202840
dc.identifier.uri http://hdl.handle.net/10138/218285
dc.description.abstract Experimental studies have revealed evidence of both parts-based and holistic representations of objects and faces in the primate visual system. However, it is still a mystery how such seemingly contradictory types of processing can coexist within a single system. Here, we propose a novel theory called mixture of sparse coding models, inspired by the formation of category-specific subregions in the inferotemporal (IT) cortex. We developed a hierarchical network that constructed a mixture of two sparse coding submodels on top of a simple Gabor analysis. The submodels were each trained with face or non-face object images, which resulted in separate representations of facial parts and object parts. Importantly, evoked neural activities were modeled by Bayesian inference, which had a top-down explaining-away effect that enabled recognition of an individual part to depend strongly on the category of the whole input. We show that this explaining-away effect was indeed crucial for the units in the face submodel to exhibit significant selectivity to face images over object images in a similar way to actual face-selective neurons in the macaque IT cortex. Furthermore, the model explained, qualitatively and quantitatively, several tuning properties to facial features found in the middle patch of face processing in IT as documented by Freiwald, Tsao, and Livingstone (2009). These included, in particular, tuning to only a small number of facial features that were often related to geometrically large parts like face outline and hair, preference and anti-preference of extreme facial features (e.g., very large/small inter-eye distance), and reduction of the gain of feature tuning for partial face stimuli compared to whole face stimuli. Thus, we hypothesize that the coding principle of facial features in the middle patch of face processing in the macaque IT cortex may be closely related to mixture of sparse coding models. en
dc.format.extent 27
dc.language.iso eng
dc.relation.ispartof PLoS Computational Biology
dc.rights cc_by
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject VISUAL-CORTEX
dc.subject RECOGNITION
dc.subject EMERGENCE
dc.subject PATCHES
dc.subject STATISTICS
dc.subject PERCEPTION
dc.subject OBJECTS
dc.subject 1182 Biochemistry, cell and molecular biology
dc.title A mixture of sparse coding models explaining properties of face neurons related to holistic and parts-based processing en
dc.type Article
dc.contributor.organization Department of Computer Science
dc.contributor.organization Neuroinformatics research group / Aapo Hyvärinen
dc.contributor.organization Helsinki Institute for Information Technology
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1371/journal.pcbi.1005667
dc.relation.issn 1553-734X
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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