Fast nonnegative deconvolution for spike train inference from population calcium imaging.
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.
Vogelstein
Joshua T
JT
Johns Hopkins University, Department of Neuroscience, 3400 N. Charles St., Baltimore, MD 21205, USA. joshuav@jhu.edu
Packer
Adam M
AM
Machado
Timothy A
TA
Sippy
Tanya
T
Babadi
Baktash
B
Yuste
Rafael
R
Paninski
Liam
L
D000200:Action Potentials; D000465:Algorithms; D020013:Calcium Signaling; D003198:Computer Simulation; D005456:Fluorescent Dyes / Q000032:analysis*; D008856:Microscopy, Fluorescence; D018715:Microscopy, Video; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology; D016011:Normal Distribution; D016012:Poisson Distribution; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors
D005456:Fluorescent Dyes