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code is all matlab unfortunately

and they don't show any real ground truth

but maybe some interesting algorithmic ideas

the biggest computational speed up seems to from the SVD (section 5)

@freeman-lab do you have an intuition for how the SVD trick works? There's some parametrization I don't understand (like why they bin time to get ~10k timepoints -- is this just to fit under a memory ceiling?)

as with many approaches, the setup is an optimization where you are minimizing the squared error between the real data and the model-generated data

and you find parameters that minimize error

if you then approximate the cost function by replacing the data part with the data including only the top k dimensions

then you might as well replace the model part in the same way

so you just end up fitting parameters to a much, much smaller data set

and yeah the binning is probably just to speed up their initial SVD calculation

my bet is, with the SVD, all the model fitting is super fast

not unlike the distributed ICA we do, where as you know the SVD step is the slowest

I wonder how many visual-cortex-specific parameters are baked into this thing

"The basis functions B do not need to be fit and are fixed as a

set of isotropic 2d raised cosine functions. The raised cosines tile the full FOV, with inter-center

spacing of ten times the diameter of a cell."

set of isotropic 2d raised cosine functions. The raised cosines tile the full FOV, with inter-center

spacing of ten times the diameter of a cell."

on a scale between "general tool for lots of imaging datasets" and "something that works for one person", my sense is that this is closer to the second pole

we can at least run it on all the neurofinder datasets!

i actually thought @marius10p was doing that

the simulated ground truth approach could be pretty useful for other methods, e.g. mika's

I'll forward this to him

then what's the simulated part?

i still think mika should run his thing on the neurofinder data first :) but yes, definitely good to share with him

"we created a simulated ground truth benchmark. We first ran the pipeline on a single experiment and manually curated the results.We then chose a small subset of cells (20 out of 300) and added their activity to a neighboring spatial location of the movie, which did not overlap significantly with the rest of the ROIs (Fig.

5a-b). We then re-ran the pipeline and compared the results with the 20 known cell locations.

We re-ran this test 43 times, choosing a different set of cells every time. The analysis shows

that almost all ground truth ROIs were correctly identified (Fig. 5c)"

5a-b). We then re-ran the pipeline and compared the results with the 20 known cell locations.

We re-ran this test 43 times, choosing a different set of cells every time. The analysis shows

that almost all ground truth ROIs were correctly identified (Fig. 5c)"

so it's not asking what fraction of a bunch of manually defined ROIs were found

more like a self-consistency check

like, if you simulate from the model, and then run identification on the simulated results

except it's not quite that, because instead of simulating from the model

they're simulating from the data conditional on the model having identified that part of the data as a cell

:confused:

I'm not sure how to interpret this measure if we don't know how the 20 cells were picked in the first place