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.


Published in:
2012 Annual International Conference Of The Ieee Engineering In Medicine And Biology Society (Embc), 4611-4614
Presented at:
34th Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS)
Year:
2012
Publisher:
New York, Ieee
ISSN:
1557-170X
ISBN:
978-1-4577-1787-1
Laboratories:




 Record created 2013-03-28, last modified 2018-01-28


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