NeuroResource Simultaneous Denoising , D econvolution , and Demixing of Calcium Imaging Data Highlights

and show the results in Figure 7. The raw data is typically dense when averaged over either time or space, as can be seen from the low intensity of the raw data correlation and mean image (Figures 7A–7C).We initialize randomly using a large number of components (in this case 50) and then order the inferred components as before. The top 23 of these components together with the background and the corresponding temporal traces in DF/F units are shown in Figures 7D–7F. These components are sparse in both space and time, with the temporal components extracting temporally localized bursts visible in the raw video data, and the spatial components extracting segments of the apparent dendritic structures visible in the video data (see also Movie S9). The results show the effectiveness of the sparse CNMFprocedure in obtaining separated spatiotemporal components given dendritic imaging data in which the degree of overlap is very high and the spatial components are not localized.

[1]  L. Paninski,et al.  Simultaneous Multi-plane Imaging of Neural Circuits , 2016, Neuron.

[2]  Timothy A. Machado,et al.  Primacy of Flexor Locomotor Pattern Revealed by Ancestral Reversion of Motor Neuron Identity , 2015, Cell.

[3]  Matthias Bethge,et al.  Supervised learning sets benchmark for robust spike rate inference from calcium imaging signals , 2015, bioRxiv.

[4]  Fred A. Hamprecht,et al.  Sparse Space-Time Deconvolution for Calcium Image Analysis , 2014, NIPS.

[5]  Takashi Kawashima,et al.  Mapping brain activity at scale with cluster computing , 2014, Nature Methods.

[6]  Attila Losonczy,et al.  SIMA: Python software for analysis of dynamic fluorescence imaging data , 2014, Front. Neuroinform..

[7]  Toru Aonishi,et al.  Detecting cells using non-negative matrix factorization on calcium imaging data , 2014, Neural Networks.

[8]  René Vidal,et al.  Structured Low-Rank Matrix Factorization: Optimality, Algorithm, and Applications to Image Processing , 2014, ICML.

[9]  E. Boyden,et al.  Simultaneous whole-animal 3D-imaging of neuronal activity using light-field microscopy , 2014, Nature Methods.

[10]  Liam Paninski,et al.  Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions , 2013, NIPS.

[11]  Adam M. Packer,et al.  Extracting regions of interest from biological images with convolutional sparse block coding , 2013, NIPS.

[12]  Fred A. Hamprecht,et al.  Learning Multi-level Sparse Representations , 2013, NIPS.

[13]  Liam Paninski,et al.  Bayesian spike inference from calcium imaging data , 2013, 2013 Asilomar Conference on Signals, Systems and Computers.

[14]  D. Fitzpatrick,et al.  Three-dimensional mapping of microcircuit correlation structure , 2013, Front. Neural Circuits.

[15]  Stefan R. Pulver,et al.  Ultra-sensitive fluorescent proteins for imaging neuronal activity , 2013, Nature.

[16]  Pier Luigi Dragotti,et al.  A finite rate of innovation algorithm for fast and accurate spike detection from two-photon calcium imaging , 2013, Journal of neural engineering.

[17]  Philipp J. Keller,et al.  Whole-brain functional imaging at cellular resolution using light-sheet microscopy , 2013, Nature Methods.

[18]  Spencer L. Smith,et al.  Parallel processing of visual space by neighboring neurons in mouse visual cortex , 2010, Nature Neuroscience.

[19]  Rafael Yuste,et al.  Fast nonnegative deconvolution for spike train inference from population calcium imaging. , 2009, Journal of neurophysiology.

[20]  Benjamin F. Grewe,et al.  High-speed in vivo calcium imaging reveals neuronal network activity with near-millisecond precision , 2010, Nature Methods.

[21]  Mark J. Schnitzer,et al.  Automated Analysis of Cellular Signals from Large-Scale Calcium Imaging Data , 2009, Neuron.

[22]  Nathalie L Rochefort,et al.  Sparsification of neuronal activity in the visual cortex at eye-opening , 2009, Proceedings of the National Academy of Sciences.

[23]  Brendon O. Watson,et al.  Spike inference from calcium imaging using sequential Monte Carlo methods. , 2009, Biophysical journal.

[24]  Brendon O. Watson,et al.  SLM Microscopy: Scanless Two-Photon Imaging and Photostimulation with Spatial Light Modulators , 2008, Frontiers in neural circuits.

[25]  Keith J. Kelleher,et al.  Three-dimensional random access multiphoton microscopy for functional imaging of neuronal activity , 2008, Nature Neuroscience.

[26]  M. Yuan,et al.  Model selection and estimation in regression with grouped variables , 2006 .