期刊
2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
卷 -, 期 -, 页码 1033-1037出版社
IEEE
关键词
Independent Component Analysis; Calcium Imaging; Data-Driven Analysis
资金
- NSF-CCF [1618551]
- NIH [DA022340, DA042595]
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1618551] Funding Source: National Science Foundation
The use of in vivo calcium imaging has granted researchers the unprecedented ability to study large populations of neurons in real time, enabling direct observation of how the brain processes information. Such data offers great potential, however for current analysis techniques, successful extraction of the true neuronal signals is intimately tied to the proper selection of multiple user-defined parameters, which must be tuned for each video sequence. To overcome such issues, we propose a novel parameter-free independent component analysis (ICA)-based method, ICA with signal reconstruction and ordering (ICA+SRO), to automatically extract neuronal signals from calcium imaging sequences. The power of ICA+SRO is demonstrated on a real calcium imaging sequence. We compare the results of ICA+SRO with those from the popular principal component analysis-ICA based technique and show significant improvement. The results demonstrate the simplicity of a parameter-free method and its power in extracting neuronal signals from calcium imaging sequences.
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