Fast online deconvolution of calcium imaging data
Janelia Research Campus · Columbia University · +3 more institutions
Abstract
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…
Citation impact
- FWCI
- 44.37
- Percentile
- 100%
- References
- 63
Authors
3Topics & keywords
- Deconvolution
- Computer science
- Calcium
- Artificial intelligence
- Algorithm
- Medicine
- Internal medicine
Funding
- NSNational Science Foundation
- UDU.S. Department of the InteriorAwards: D16PC00007, D16PC00003
- SFSimons FoundationAwards: 365002, 325171
- SNSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungAward: P300P2_158428
- NINational Institutes of HealthAwards: 2R01MH064537, R01 EB22913, R21 EY027592, D16PC00007, R90DA023426
- DADefense Advanced Research Projects AgencyAward: N66001-15-C-4032
- ARAdvanced Research Projects AgencyAward: W911NF-12-1-0594
- IAIntelligence Advanced Research Projects ActivityAwards: D16PC00007, D16PC00003, D16PC00008
- IBInterior Business Center
- MUMultidisciplinary University Research InitiativeAward: W911NF-12-1-0594
- ARArmy Research OfficeAwards: MURI W911NF-12-1-0594, W911NF-12-1-0594, W911NF