Spline- and wavelet-based models of neural activity in response to natural visual stimulation.
We present a comparative study of the performance of different basis functions for the nonparametric modeling of neural activity in response to natural stimuli. Based on naturalistic video sequences, a generative model of neural activity was created using a stochastic linear-nonlinear-spiking cascade. The temporal dynamics of the spiking response is well captured with cubic splines with equidistant knot spacings. Whereas a sym4-wavelet decomposition performs competitively or only slightly worse than the spline basis, Haar wavelets (or histogram-based models) seem unsuitable for faithfully describing the temporal dynamics of the sensory neurons. This tendency was confirmed with an application to a real data set of spike trains recorded from visual cortex of the awake monkey.
Brain Mind Institute, Ecole Polytechnique Federale de Lausanne, 1015 Lausanne EPFL, Switzerland. felipe.gerhard@epfl.ch
Felipe
F
Gerhard
Luca
L
Szegletes
D000818:Animals; D003198:Computer Simulation; D005074:Evoked Potentials, Visual / Q000502:physiology*; D000882:Haplorhini; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D010775:Photic Stimulation / Q000379:methods*; D014793:Visual Cortex / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*; D058067:Wavelet Analysis