“…This is a difficult statistical problem for a number of reasons, including www.cshprotocols.org the low temporal resolution of the imaging (often <30 Hz) and, in some cases, the limited signal-to-noise ratio of the fluorescence observations (e.g., caused by indicator saturation). A number of groups have devised algorithms for extracting spiking activity from fluorescence observations, including thresholding (Mao et al 2001), template matching (Kerr et al 2005), linear deconvolution (Yaksi and Friedrich 2006;Holekamp et al 2008), support vector methods (Sasaki et al 2008), nonlinear Bayesian deconvolution via sequential Monte Carlo (particle filtering) (Vogelstein et al 2009), and fast non-negatively constrained optimization methods (Vogelstein et al 2010). Most of these approaches either explicitly or implicitly assume a model relating the spike trains to the fluorescence observations: The spike train is convolved with some linear filter (usually a simple exponential filter, although this may be generalized) to obtain the intracellular calcium concentration, and the fluorescence signal is essentially a noisy, possibly saturated version of the calcium.…”