TY - GEN
T1 - Stochastic filtering of two-photon imaging using reweighted ℓ1
AU - Charles, Adam S.
AU - Song, Alexander
AU - Koay, Sue Ann
AU - Tank, David W.
AU - Pillow, Jonathan W.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - Two-photon (TP) calcium imaging is an important imaging modality in neuroscience, allowing for large-scale recording of neural activity in awake, behaving animals at behavior-relevant timescales. Interpretation of TP data requires the accurate extraction of temporal neural activity traces, which can be accomplished via manual or automated methods. In this work we seek to improve the accuracy of both manual and automated TP microscopy demixing methods by introducing a denoising algorithm based on a statistical model of TP data which includes spatial contiguity, sparse activity and Poisson observations. Our method leverages recent developments in stochastic filtering of structured signals based on Laplacian-scale mixture models (LSMs) to model the neural activity in TP data as a set of spatially correlated sparse variables. We apply our method on TP images taken from the visual cortex of an awake, behaving mouse, and demonstrate improved neural activity demixing over current pre-processing techniques.
AB - Two-photon (TP) calcium imaging is an important imaging modality in neuroscience, allowing for large-scale recording of neural activity in awake, behaving animals at behavior-relevant timescales. Interpretation of TP data requires the accurate extraction of temporal neural activity traces, which can be accomplished via manual or automated methods. In this work we seek to improve the accuracy of both manual and automated TP microscopy demixing methods by introducing a denoising algorithm based on a statistical model of TP data which includes spatial contiguity, sparse activity and Poisson observations. Our method leverages recent developments in stochastic filtering of structured signals based on Laplacian-scale mixture models (LSMs) to model the neural activity in TP data as a set of spatially correlated sparse variables. We apply our method on TP images taken from the visual cortex of an awake, behaving mouse, and demonstrate improved neural activity demixing over current pre-processing techniques.
KW - Reweighted ℓ
KW - Stochastic filtering
KW - Two-photon microscopy
UR - http://www.scopus.com/inward/record.url?scp=85023755567&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85023755567&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2017.7952314
DO - 10.1109/ICASSP.2017.7952314
M3 - Conference contribution
AN - SCOPUS:85023755567
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1038
EP - 1042
BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Y2 - 5 March 2017 through 9 March 2017
ER -