Sparse MAP inference of cell shapes and spike trains from calcium imaging data
| Project/Area Number |
26870577
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Brain biometrics
Intelligent informatics
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| Research Institution | Kogakuin University |
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Principal Investigator |
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| Project Period (FY) |
2014-04-01 – 2016-03-31
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| Project Status |
Completed (Fiscal Year 2015)
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| Budget Amount *help |
¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2014: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | カルシウムイメージング / 信号検出 / 信号源検出 / スパイク時系列 / モデル推定 / 大規模最適化問題 / セルソーティング / スパース性 / MAP推定 / 最適化 / 逐次二次計画法 / 内点法 / 共役勾配法 |
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| Outline of Final Research Achievements |
Calcium imaging has become popular as data acquisition technique from neural circuits and the scale of imaging data is rapidly increasing. In this paper, we present an automatic cell sorting algorithm which detects cell positions and spike timings simultaneously from large scale calcium imaging data. Our algorithm is based on a simple probabilistic model with random variables for spatial and temporal activities of each cell. The model also contains spatial and temporal baseline as random variables and these variables can be effectively inferred by iterative quadratic programming problems with few control parameters for sparseness. Present inference procedure can detect separately spiking activities from from spatially overlapped neurons which activities contains cross-talk components. Faster implementation of the approach have been also developed by tuning of numerical solver routine using the problem specific characteristic.
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Report
(3 results)
Research Products
(5 results)
