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dc.contributor.authorTraver Roig, Vicente Javier
dc.contributor.authorPla, Filiberto
dc.contributor.authorMiquel, Marta
dc.contributor.authorCarbó Gas, María
dc.contributor.authorGil-Miravet, Isis
dc.contributor.authorGuarque-Chabrera, Julian
dc.date.accessioned2019-02-21T10:38:34Z
dc.date.available2019-02-21T10:38:34Z
dc.date.issued2018
dc.identifier.citationTraver, V.J., Pla, F., Miquel, M. et al. Neuroinform (2018). https://doi.org/10.1007/s12021-018-9401-1ca_CA
dc.identifier.issn1539-2791
dc.identifier.issn1559-0089
dc.identifier.urihttp://hdl.handle.net/10234/181447
dc.description.abstractExisting work on drug-induced synaptic changes has shown that the expression of perineuronal nets (PNNs) at the cerebellar cortex can be regulated by cocaine-related memory. However, these studies on animals have mostly relied on limited manually-driven procedures, and lack some more rigorous statistical approaches and more automated techniques. In this work, established methods from computer vision and machine learning are considered to build stronger evidence of those previous findings. To that end, an image descriptor is designed to characterize PNNs images; unsupervised learning (clustering) is used to automatically find distinctive patterns of PNNs; and supervised learning (classification) is adopted for predicting the experiment group of the mice from their PNN images. Experts in neurobiology, who were not aware of the underlying computational procedures, were asked to describe the patterns emerging from the automatically found clusters, and their descriptions were found to align surprisingly well with the two types of PNN images revealed from previous studies, namely strong and weak PNNs. Furthermore, when the set of PNN images corresponding to every mice in the saline (control) group and the conditioned (experimental) group were characterized using a bag-of-words representation, and subject to supervised learning (saline vs conditioned mice), the high classification results suggest the ability of the proposed representation and procedures in recognizing these groups. Therefore, despite the limited size of the dataset (1,032 PNN images of 6 saline and 6 conditioned mice), the results support existing evidence on the drug-related brain plasticity, while providing higher objectivityca_CA
dc.format.extent17 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherSpringerca_CA
dc.relation.isPartOfNeuroinform (2018)ca_CA
dc.rights© Springer Science+Business Media, LLC, part of Springer Nature 2018ca_CA
dc.subjectCerebellumca_CA
dc.subjectPerineuronal netsca_CA
dc.subjectDrug-related memoryca_CA
dc.subjectComputer visionca_CA
dc.subjectMachine learningca_CA
dc.subjectUnsupervised learningca_CA
dc.subjectSupervised learningca_CA
dc.titleCocaine-Induced Preference Conditioning: a Machine Vision Perspectiveca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1007/s12021-018-9401-1
dc.relation.projectIDP1.1B2014-09 ; PSI2015-68600-P ; FPU12/04059 ; PREDOC2014/11ca_CA
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttps://link.springer.com/article/10.1007/s12021-018-9401-1ca_CA
dc.date.embargoEndDate2019-10-25
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA


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