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Application of Penalized Splines in Analyzing Neuronal Data
Author(s) -
Maringwa John T.,
Faes Christel,
Geys Helena,
Molenberghs Geert,
CadarsoSuárez Carmen,
PardoVázquez José L.,
Leborán Víctor,
Acunña Carlos
Publication year - 2009
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200810501
Subject(s) - smoothing , inference , population , moment (physics) , mathematics , smoothing spline , confidence and prediction bands , statistical inference , extension (predicate logic) , computer science , algorithm , confidence interval , statistics , artificial intelligence , demography , classical mechanics , sociology , bilinear interpolation , programming language , spline interpolation , physics
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 (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)