Mixed selectivity cells: c11m1d13

[kimtonyhyun]

@ahwillia As a first look at mixed selectivity in the mPFC dataset, I shifted manually through c11m1d13 for cells that showed the phenotype at a single-cell raster level.

As an aside, we may want to consider a different name for these cells in our dataset. I think our phenotype is somewhat distinct from "mixed selectivity" of Fusi et al. in that the different factors are at play in a single cell at different times. (I think this is a good short summary of their definition.) We can discuss more in person.

To follow along, go to __Dropbox/schnitzer_ganguli/c11m1d13__ in Matlab:

1. Load the "cm01-fix" extraction set into a DaySummary instance.
2. Load the "cm01-fix_mixed-cells.mat" into the Matlab workspace. It contains mixed_cells, which is a list of 27 cells (out of 507; around 5% of the population) that I considered to be "mixed selectivity cells" based on their rasters.

   
   >> sources = data_sources
   sources = 
        maze: '_data/c11m1d13_ti2.txt'
    behavior: '_data/c11m1d13_ti2.mp4'
    tracking: '_data/c11m1d13_ti2.xy'
   miniscope: '_data/c11m1d13_gfix_rm_mc_cr_norm40_dff_ti2.hdf5'

m1d13 = DaySummary(sources, 'cm01-fix', 'excludeprobe'); 28-Jan-2017 09:03:53: Loaded 507 filters and traces (export_rec) from cm01-fix\rec_161123-111930.mat 28-Jan-2017 09:03:53: Loaded trial metadata from _data/c11m1d13_ti2.txt Computing auxiliary spatial parameters... Done (5.0 sec) Computing distances between all sources... Done (0.2 sec) 28-Jan-2017 09:04:00: Loaded classification from cm01-fix\class_161123-121037.txt 28-Jan-2017 09:04:03: Loaded behavior video from "_data/c11m1d13_ti2.mp4" 28-Jan-2017 09:04:03: Loaded tracking data from "_data/c11m1d13_ti2.xy"

load('cm01-fix_mixed-cells.mat')

You can then run browse_rasters on the mixed-selectivity subset of cells as:

>> browse_rasters(m1d13, 'cells', mixed_cells);
Raster browser (page 1 of 4) >> 

Here are the 27 single-cell rasters:

Among these, I think Cells 69, 278, 310 and 497 have particularly high "fidelity". Here are their detailed single-cell rasters. Feel to speculate on what their interpretations might be :grin::

[kimtonyhyun]

I then performed the CPD analysis:

The best r-15 model, trial vector colored by correctness:

The results can be obtained here:

• c11m1d13-export.mat: Contains the exported data tensor X;
• c11m1d13-nncpd_r15_run01.mat: Contains the best-performing r-15 model cpd and the reconstructed tensor Xest.

I then used eval_fit to consider the quality of CPD fit against the cells 69, 278, 310 and 497.

Cell 69 (Rsq=0.4315). I sorted the individual trials by residual:

Cell 278 (Rsq=0.5841):

Cell 310 (Rsq=0.6086):

Cell 497 (Rsq=0.5423);

Based on these examples, I don't think the r-15 CPD fit is doing an adequate job fitting the exemplary mixed-selectivity cells in c11m1d13. The reason for this could be that the example neurons have Ca2+ traces with transient dynamics, which is not adequately captured by the CPD model.

Thoughts?

[kimtonyhyun]

Here is a distribution of single-cell Rsq values for the 27 mixed selectivity cells of c11m1d13:

My rough threshold for a "good fit" is Rsq > 0.7. Out of the 27 cells, only one reaches this value, and the rest are at the bad-to-okay range.

Here is the best-fit example, Cell 477 (Rsq=0.7321). Individual trials sorted by residual:

Here is the corresponding raster plot for that cell. It seems to me that this cell has the least-transient-like trace among the 27: