Spike inference from calcium imaging using sequential Monte Carlo
methods
Joshua Vogelstein, Brendon Watson, Adam Packer, Bruno Jedynak, Rafael
Yuste, and Liam Paninski
Biophysical Journal 97: 636-655
As recent advances in calcium sensing technologies enable us to
simultaneously image many neurons, complementary analytical tools must
also be developed to maximize the utility of this experimental
paradigm. While the observations here are fluorescence images, the
signals of interest - spike trains and/or time varying intracellular
calcium concentrations - are hidden. Inferring these hidden signals is
often problematic due to noise, nonlinearities, slow imaging rate, and
unknown biophysical parameters. We overcome these difficulties by
developing a family of particle filters based on biophysical models of
spiking, calcium dynamics, and fluorescence. We show that even in
simple cases, the particle filters outperform the optimal linear
(i.e., Wiener) filter, both by obtaining better estimates and by
providing errorbars. We then relax a number of our model assumptions
to incorporate nonlinear saturation of the fluorescence signal, as
well external stimulus and spike history dependence of the spike
trains. Using both simulations and in vitro fluorescence
observations, we demonstrate superresolution by inferring when within
a frame each spike occurs. Furthermore, the model parameters may be
estimated using expectation-maximization with only a very limited
amount of data, without the requirement of any simultaneous
electrophysiology and imaging experiments.
Preprint (pdf, 700K) | Liam Paninski's home