Cocaine-Induced Preference Conditioning: a Machine Vision Perspective
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Otros documentos de la autoría: Traver Roig, Vicente Javier; Pla, Filiberto; MIQUEL, MARTA; Carbó Gas, María; Gil-Miravet, Isis; Guarque-Chabrera, Julian
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Título
Cocaine-Induced Preference Conditioning: a Machine Vision PerspectiveAutoría
Fecha de publicación
2018Editor
SpringerISSN
1539-2791; 1559-0089Cita bibliográfica
Traver, V.J., Pla, F., Miquel, M. et al. Neuroinform (2018). https://doi.org/10.1007/s12021-018-9401-1Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/article/10.1007/s12021-018-9401-1Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Existing 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 ... [+]
Existing 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 objectivity [-]
Publicado en
Neuroinform (2018)Proyecto de investigación
P1.1B2014-09 ; PSI2015-68600-P ; FPU12/04059 ; PREDOC2014/11Derechos de acceso
© Springer Science+Business Media, LLC, part of Springer Nature 2018
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info:eu-repo/semantics/openAccess
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