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Multidimensional adaptive P-splines with application to neurons' activity studies

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorEilers, Paul H.C.
dc.contributor.authorDurbán Reguera, María Luz
dc.contributor.authorRodríguez-Alvarez, María Xosé
dc.contributor.authorLee, Dae-Jin
dc.contributor.authorGonzález, Francisco
dc.date.accessioned2024-01-22T16:08:37Z
dc.date.available2024-01-22T16:08:37Z
dc.date.issued2023-09-01
dc.description.abstractThe receptive field (RF) of a visual neuron is the region of the space that elicits neuronal responses. It can be mapped using different techniques that allow inferring its spatial and temporal properties. Raw RF maps (RFmaps) are usually noisy, making it difficult to obtain and study important features of the RF. A possible solution is to smooth them using P-splines. Yet, raw RFmaps are characterized by sharp transitions in both space and time. Their analysis thus asks for spatiotemporal adaptive P-spline models, where smoothness can be locally adapted to the data. However, the literature lacks proposals for adaptive P-splines in more than two dimensions. Furthermore, the extra flexibility afforded by adaptive P-spline models is obtained at the cost of a high computational burden, especially in a multidimensional setting. To fill these gaps, this work presents a novel anisotropic locally adaptive P-spline model in two (e.g., space) and three (space and time) dimensions. Estimation is based on the recently proposed SOP (Separation of Overlapping Precision matrices) method, which provides the speed we look for. Besides the spatiotemporal analysis of the neuronal activity data that motivated this work, the practical performance of the proposal is evaluated through simulations, and comparisons with alternative methods are reported.es
dc.identifier.bibliographicCitationMultidimensional Adaptive P-Splines with Application to Neurons' Activity Studies, Biometrics, 79 (3), 2023, 1972–1985es
dc.identifier.doihttps://doi.org/10.1111/biom.13755
dc.identifier.issn0006-341X
dc.identifier.publicationfirstpage1972es
dc.identifier.publicationissue3es
dc.identifier.publicationlastpage1985es
dc.identifier.publicationtitleBIOMETRICSes
dc.identifier.publicationvolume79es
dc.identifier.urihttps://hdl.handle.net/10016/39414
dc.identifier.uxxiAR/0000033804
dc.language.isoenges
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaEstadísticaes
dc.subject.otherAnisotropyes
dc.subject.otherLocal Adaptivityes
dc.subject.otherPenalized Splineses
dc.subject.otherSmoothinges
dc.subject.otherVisual Receptive Fieldses
dc.titleMultidimensional adaptive P-splines with application to neurons' activity studieses
dc.type.hasVersionVoRes
dspace.entity.typePublication
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