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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/9512

Title: Application of Penalized Splines in Analyzing Neuronal Data
Authors: Maringwa, John
Faes, Christel
Geys, Helena
Molenberghs, Geert
Cadarso-Suarez, Carmen
Pardo-Vazquez, Jose L.
Leboran, Victor
Acuna, Carlos
Issue Date: 2009
Publisher: WILEY-V C H VERLAG GMBH
Citation: BIOMETRICAL JOURNAL, 51(1). p. 203-216
Abstract: Neuron experiments produce high-dimensional data structures. Therefore, application of smoothing techniques in the analysis of neuronal data from electrophysiological experiments has received considerable attention of late. We investigate the use of penalized splines in the analysis of neuronal data. This is first illustrated when interested in the temporal trend of a single neuron. An approach to investigate the maximal firing rate, based on the penalizedspline model is proposed. Determination of the time of maximal firing rate is based on non-linear optimization of the objective function with the corresponding confidence intervals constructed based on the first-order derivative function. To distinguish between the curves from different experimental conditions in a moment-by-moment sense, bias adjusted simulation-based simultaneous confidence bands leading to global inference in the time domain are constructed. The bands are an extension of the approach proposed by Ruppert et al. (2003). These methods are in a second step extended towards the analysis of a population of neurons via a marginal or population-averaged model.
Notes: [Maringwa, John T.; Faes, Christel; Molenberghs, Geert] Hasselt Univ, Ctr Stat, BE-3590 Diepenbeek, Belgium. [Geys, Helena] Johnson & Johnson Pharmaceut Res & Dev, Beerse, Belgium. [Cadarso-Suarez, Carmen] Univ Santiago de Compostela, Dept Stat & Operat Res, Santiago De Compostela, Spain. [Pardo-Vazquez, Jose L.; Leboran, Victor; Acuna, Carlos] Univ Santiago de Compostela, Complejo Hosp Univ, Fac Med, Dept Fisiol, Santiago De Compostela, Spain.
URI: http://hdl.handle.net/1942/9512
DOI: 10.1002/bimj.200810501
ISI #: 000264082000017
ISSN: 0323-3847
Category: A1
Type: Journal Contribution
Validation: ecoom, 2010
Appears in Collections: Research publications

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