The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Fast online deconvolution of calcium imaging data
Open accessAbstract
Fluorescent calcium indicators are a popular means for observing the spiking activity of large neuronal populations, but extracting the activity of each neuron from raw fluorescence calcium imaging data is a nontrivial problem. We present a fast online active set method to solve this sparse non-negative deconvolution problem. Importantly, the algorithm 3progresses through each time series sequentially from beginning to end, thus enabling real-time online estimation of neural activity during the imaging session. Our algorithm is a generalization of the pool adjacent violators algorithm (PAVA) for isotonic regression and inherits its linear-time computational complexity. We gain remarkable increases in processing speed: more than one order of magnitude compared to currently employed state of the art convex solvers relying on interior point methods. Unlike these approaches, our method can exploit warm starts; therefore optimizing model hyperparameters only requires a handful of passes through the data. A minor modification can further improve the quality of activity inference by imposing a constraint on the minimum spike size. The algorithm enables real-time simultaneous deconvolution of O(105) traces of whole-brain larval zebrafish imaging data on a laptop.
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Cites
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Fast nonnegative deconvolution for spike train inference from population calcium imaging
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Self-citationLight-sheet functional imaging in fictively behaving zebrafish
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Spontaneous behaviors drive multidimensional, brainwide activity
Stringer, C., Pachitariu, M., Steinmetz, N., Reddy, C. B., Carandini, M., & Harris, K. D. (2019) · Science · doi:10.1126/science.aav7893 · Cited by 1,510
Suite2p: beyond 10,000 neurons with standard two-photon microscopy
Pachitariu, M., Stringer, C., Dipoppa, M., Schröder, S., Rossi, L. F., Dalgleish, H., Carandini, M., & Harris, K. D. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/061507 · Cited by 1,247
High-dimensional geometry of population responses in visual cortex
Stringer, C., Pachitariu, M., Steinmetz, N., Carandini, M., & Harris, K. D. (2019) · Nature · doi:10.1038/s41586-019-1346-5 · Cited by 594
Yu, X., Taylor, A. M. W., Nagai, J., Golshani, P., Evans, C. J., Coppola, G., & Khakh, B. S. (2018) · Neuron · doi:10.1016/j.neuron.2018.08.015 · Cited by 398
The gut–brain axis mediates sugar preference
Tan, H.-E., Sisti, A. C., Jin, H., Vignovich, M., Villavicencio, M., Tsang, K. S., Goffer, Y., & Zuker, C. S. (2020) · Nature · doi:10.1038/s41586-020-2199-7 · Cited by 312
Metadata
- Authors
- Friedrich, J., Zhou, P., & Paninski, L.
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2017-03-14
- Volume
- 13
- Issue
- 3
- Pages
- e1005423
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- OpenAlex
- W2595012769
- Cited by
- 559
- References
- 6
- FWCI
- 17.24
- Type
- journal article
- Language
- en
- Open access
- gold
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