Parameter-free automated extraction of neuronal signals from calcium imaging data
Yuri Levin-Schwartz, Dennis R. Sparta, Joseph F. Cheer, Tülay Adali
Abstract
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.
BibTeX
@inproceedings{icassp2017_parameterfreeaut,
title = {Parameter-free automated extraction of neuronal signals from calcium imaging data},
author = {Yuri Levin-Schwartz and Dennis R. Sparta and Joseph F. Cheer and Tülay Adali},
booktitle = {ICASSP 2017},
year = {2017}
}