Fast non-negative deconvolution for spike train inference from
population calcium imaging
Joshua Vogelstein, Adam Packer, Timothy Machado, Tanya Sippy,
Baktash Babadi, Rafael
Yuste, and Liam Paninski
In press, J. Neurophys.
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 non-negative 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, like the Wiener
filter, and is fast enough that even when imaging over 100 neurons
simultaneously, 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.
Preprint (pdf, 700K) | Liam Paninski's home