Fast nonnegative deconvolution for spike train inference from population calcium imaging
Open accessAbstract
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.
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The fast nonnegative deconvolution filter approximates the likely spike train of each neuron given the fluorescence observations.
“This method has been inferred to approximate the likely spike train of each neuron, given the fluorescence observations.”
From Neuronal Representation of Social Information in the Medial Amygdala of Awake Behaving Mice (2017) · cites this paper as Vogelstein et al., 2010
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- Authors
- Vogelstein, J. T., Packer, A. M., Machado, T. A., Sippy, T., Babadi, B., Yuste, R., & Paninski, L.
- Affiliations
- Johns Hopkins University (US)
- Howard Hughes Medical Institute (US)
- Center for Theoretical Neuroscience, and
- Columbia University (US)
- Corresponding author
- Vogelstein, Joshua T.
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 2010-12
- Volume
- 104
- Issue
- 6
- Pages
- 3691-3704
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- PMC
- PMC3007657
- OpenAlex
- W2067474937
- Cited by
- 449
- References
- 57
- Citations by year
- 2026: 13 · 2025: 20 · 2024: 28 · 2023: 20 · 2022: 32 · 2021: 40 · 2020: 35 · 2019: 41 · 2018: 37 · 2017: 38 · 2016: 45 · 2015: 31 · 2014: 30 · 2013: 18 · 2012: 9
- FWCI
- 7.56
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Neurophysiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Howard Hughes Medical Institute
- National Eye Institute (EY-11787, R01EY011787)
- National Institute on Deafness and Other Communication Disorders (DC-00109)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (T32HD007430)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Advanced Fluorescence Microscopy Techniques
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- calcium imaging, fluorescent calcium indicators, Wiener filter, interior point methods, spatial filtering, neural population activity, real-time inference
- MeSH terms
- Action Potentials, Algorithms, Computer Simulation, Fluorescent Dyes, Microscopy, Fluorescence, Models, Neurological, Neurons, Signal Processing, Computer-Assisted, Time Factors, Normal Distribution, Poisson Distribution, Microscopy, Video, Calcium Signaling
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