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@epnev epnev released this 27 Jun 16:29
· 366 commits to master since this release
v0.4.2
950ba9a

New Features

  • The dataset is normalized by the noise value before the initialization to deal with cell detection in the darker areas of the field of view. To use this feature pass P as a fifth input argument in initialize_components.m (default)
  • New initialization method greedy_corrbased on the correlation image developed from PC Zhou @zhoupc To use it set options.init_method = 'greedy_corr'
  • New initialization method HALS based only on constrained NMF iterations. To use it set options.init_method = 'HALS'
  • The user can now seed the algorithm initialization by providing a K x 2 matrix with the centroids of the cells. To use this feature pass the centroid matrix as P.ROI_list and pass P as a fifth input argument in initialize_components.m
  • New plotting and post-processing tools through [postProcessCNMF.m]. Developed from W.Yang @NTCColumbia (https://github.com/epnev/ca_source_extraction/blob/master/postProcessCNMF.m) developed from W.Yang @NTCColumbia
  • New ordering method order_components.m

Modifications

  • initialize_components.m can optionally take P as a fifth input argument for data normalization and/or user seeded initialization.
  • extract_DF_F.m does not take as an input the neural activity signal S and it no longer produces S_dfas an output variable.
  • Better memory management from update_spatial_compononents.m for handling large datasets.
  • Faster implementation of correlation_image.m, HALS_temporal.m and HALS_spatial.m from @zhoupc

Acknowledgements

Special thanks to Pengcheng Zhou @zhoupc and Weijian Yang @NTCColumbia for their contributions.

Assets 2