ArticleBiostatistics (Oxford, England)2020
Fast nonconvex deconvolution of calcium imaging data.
Article in Biostatistics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed.
- Evaluating linear model frameworks for directed predictive influence estimation from hypothalamic calcium imaging.Journal of neuroscience methods · 2026Article
- Sequential Change Point Detection in Dynamic Non-Stationary Manufacturing Processes.INFORMS journal on data science · 2026Article
- CA1 Engram Cell Dynamics Before and After Learning.bioRxiv : the preprint server for biology · 2026Article
- Article
- Neural circuit models for evidence accumulation through choice-selective sequences.Nature communications · 2026Article
- Unraveling the neural code: analysis of large-scale two-photon microscopy data.Microscopy (Oxford, England) · 2025Review
- Predicting neuronal firing from calcium imaging using a control theoretic approach.PLoS computational biology · 2025Article
- Sensory responses of olfactory and vomeronasal neurons in does are influenced by buck odors and their reproductive status.Chemical senses · 2025Article
- Vector coding and place coding in hippocampus share a common directional signal.Nature communications · 2024Article
- Multivariate Time Series Change-Point Detection with a Novel Pearson-like Scaled Bregman Divergence.Stats · 2024Article
- TIME-VARYINGData science in science · 2024Article
- Review
- Network models to enhance the translational impact of cross-species studies.Nature reviews. Neuroscience · 2023Review
- Effective and efficient neural networks for spike inference fromCell reports methods · 2023Article
- Neural engineering with photons as synaptic transmitters.Nature methods · 2023Article
- Quantifying uncertainty in spikes estimated from calcium imaging data.Biostatistics (Oxford, England) · 2023Article
- Super-resolution with Binary Priors: Theory and Algorithms.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2023Article
- Modeling and forecasting of at home activity in older adults using passive sensor technology.Statistics in medicine · 2022Article
- Review of data processing of functional optical microscopy for neuroscience.Neurophotonics · 2022Review
- Testing for a Change in Mean After Changepoint Detection.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2022Article
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4 authors.
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Abstract
Calcium imaging data promises to transform the field of neuroscience by making it possible to record from large populations of neurons simultaneously. However, determining the exact moment in time at which a neuron spikes, from a calcium imaging data set, amounts to a non-trivial deconvolution problem which is of critical importance for downstream analyses. While a number of formulations have been proposed for this task in the recent literature, in this article, we focus on a formulation recently proposed in Jewell and Witten (2018. Exact spike train inference via $\ell_{0} $ optimization. The Annals of Applied Statistics12(4), 2457-2482) that can accurately estimate not just the spike rate, but also the specific times at which the neuron spikes. We develop a much faster algorithm that can be used to deconvolve a fluorescence trace of 100 000 timesteps in less than a second. Furthermore, we present a modification to this algorithm that precludes the possibility of a "negative spike". We demonstrate the performance of this algorithm for spike deconvolution on calcium imaging datasets that were recently released as part of the $\texttt{spikefinder}$ challenge (http://spikefinder.codeneuro.org/). The algorithm presented in this article was used in the Allen Institute for Brain Science's "platform paper" to decode neural activity from the Allen Brain Observatory; this is the main scientific paper in which their data resource is presented. Our $\texttt{C++}$ implementation, along with $\texttt{R}$ and $\texttt{python}$ wrappers, is publicly available. $\texttt{R}$ code is available on $\texttt{CRAN}$ and $\texttt{Github}$, and $\texttt{python}$ wrappers are available on $\texttt{Github}$; see https://github.com/jewellsean/FastLZeroSpikeInference.
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