In:
Journal of Neurophysiology, American Physiological Society, Vol. 104, No. 6 ( 2010-12), p. 3691-3704
Kurzfassung:
Fluorescent calcium indicators are becoming increasingly popular as a means for observing the spiking activity of large neuronal populations. Unfortunately, extracting the spike train of each neuron from a raw fluorescence movie is a nontrivial problem. This work presents a fast nonnegative deconvolution filter to infer the approximately most likely spike train of each neuron, given the fluorescence observations. This algorithm outperforms optimal linear deconvolution (Wiener filtering) on both simulated and biological data. The performance gains come from restricting the inferred spike trains to be positive (using an interior-point method), unlike the Wiener filter. The algorithm runs in linear time, and is fast enough that even when simultaneously imaging 〉 100 neurons, inference can be performed on the set of all observed traces faster than real time. Performing optimal spatial filtering on the images further refines the inferred spike train estimates. Importantly, all the parameters required to perform the inference can be estimated using only the fluorescence data, obviating the need to perform joint electrophysiological and imaging calibration experiments.
Materialart:
Online-Ressource
ISSN:
0022-3077
,
1522-1598
DOI:
10.1152/jn.01073.2009
RVK:
XA 10000 ; XA 552555
Sprache:
Englisch
Verlag:
American Physiological Society
Publikationsdatum:
2010
ZDB Id:
80161-6
ZDB Id:
1467889-5
