Fast nonnegative deconvolution for spike train inference from population calcium imaging.

By Joshua T. Vogelstein, Adam Packer, Timothy A. Machado, Tanya Sippy, Baktash Babadi, Rafael Yuste, Liam Paninski. Journal of Neurophysiology, 2009

459 citations recorded by the source corpus.

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.

Subfields: Biophysics, Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/03f6f5861d790198bf38b8de08df68eefef6f0af

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Simultaneous multi-plane imaging of neural circuits (2016, unknown): “We use a statistical model to relate the detected fluorescence from a source (neuron) to the underlying activity (spiking) (Vogelstein et al., 2009; Vogelstein et al., 2010).”
Suite2p: beyond 10,000 neurons with standard two-photon microscopy (2016, methodology): “However, based on a quantitative comparison of multiple methods 24 , we recommend by default unconstrained non-negative deconvolution using exponential kernels 25–27 with a kernel decay timescale that matches the value reported in the literature for the particular calcium sensor used.”
Parallel processing by cortical inhibition enables context-dependent behavior (2016, unknown): “Deconvolution of calcium signals was performed and used for all comparisons across cells 53 .”
Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data (2016, unknown): “Our CD approach builds upon and extends the fast nonnegative deconvolution (FOOPSI) method of Vogelstein et al., (2010) in several ways.”
Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data (2018, unknown): “The neural activity si is nonnegative and typically sparse; to enforce sparsity, we can penalize the ‘0 (Jewell and Witten, 2017) or ‘1 (Pnevmatikakis et al., 2016; Vogelstein et al., 2010) norm of si, or limit the minimum size of nonzero spike counts (Friedrich et al.”
Automated long-term recording and analysis of neural activity in behaving animals (2016, background): “…imaging also has relatively poor temporal resolution (Grienberger and Konnerth, 2012; Vogelstein et al., 2009), limiting its ability to resolve precise spike patterns (Vogelstein et al., 2010; Yaksi and Friedrich, 2006) (but see Gong et al., 2015 for an alternative high-speed voltage sensor).”
Tracking the Same Neurons across Multiple Days in Ca2+ Imaging Data (2017, methodology): “While numerous methods for detecting cells and extracting their activity from Ca2+ imaging data have been developed (Reidl et al., 2007; Vogelstein et al., 2009, 2010; Mukamel et al., 2009; Grewe et al., 2010; Smith and Häusser, 2010; Oñativia et al., 2013; Pachitariu et al., 2013; Maruyama et…”
Synaptic sampling: A connection between PSP variability and uncertainty explains neurophysiological observations (2015, methodology): “Firing rate was measured by taking the average signal from a spike deconvolution algorithm [25].”
cvxEDA: A Convex Optimization Approach to Electrodermal Activity Processing (2016, methodology): “The applicability of our model is not limited to EDA analysis, but can be extended to other domains requiring the deconvolution of pulse trains from the output of systems that can be represented as ARMA models, for example, in calcium imaging [24] or hormone secretion analysis [27].”
Fast online deconvolution of calcium imaging data (2016, methodology): “Current algorithms employ interior point methods to solve the ensuing optimization problem and are fast enough to process hundreds of neurons in about the same time as the recording [5], but will not scale to currently obtained larger data sets such as whole-brain zebrafish imaging.”