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  • Feb 10 2021 15:31
    YijunBao commented #29
  • Oct 02 2020 09:48
    waseemabbas05 opened #29
  • Aug 19 2019 14:45
    pgleeson opened #28
  • Apr 10 2019 07:38

    freeman-lab on master

    add missing argument (compare)

  • Apr 10 2019 07:37

    freeman-lab on master

    finish fixes for db change (compare)

  • Feb 12 2019 01:36
    DenisPolygalov commented #27
  • Jan 02 2019 16:05
    somsol closed #26
  • Jan 02 2019 15:28
    somsol reopened #26
  • Dec 03 2018 19:57
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  • Nov 07 2018 18:10
    somsol commented #27
  • Oct 29 2018 20:43
    quic0 commented #27
  • Oct 29 2018 20:23
    freeman-lab commented #27
  • Oct 16 2018 21:38
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  • Oct 05 2018 12:09
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  • Aug 31 2018 20:50
    agiovann opened #27
  • Jul 03 2018 17:00
    somsol closed #26
  • Jul 02 2018 18:51

    freeman-lab on master

    minor fixes for db updates (compare)

  • Jul 02 2018 14:02
    somsol opened #26
  • Jun 14 2017 13:05
    alexklibisz closed #25
  • Jun 14 2017 13:05
    alexklibisz commented #25
Jeremy Freeman
@freeman-lab
that info is available in the info.json file that comes with each dataset when you download it
Alex Eusman
@Aeusman
Gotcha, so all the datasets are a single slice over time?
Jeremy Freeman
@freeman-lab
yup, exactly
Alex Eusman
@Aeusman
Thanks!
Jeremy Freeman
@freeman-lab
just updated in the README here https://github.com/codeneuro/neurofinder
also FYI we'll be posting two new datasets later today
and at that point the core neurofinder datasets will be finalized
Jeremy Freeman
@freeman-lab
i'm gonna remove the algorithms already submitted cause they were just test cases anyway, and would require updating for the new data
@jwittenbach / steve neurwin once i'm done maybe you can rerun yours to make sure everything is functioning with the new data
Jason Wittenbach
@jwittenbach
sure thing! Steve Neurwin is always at the ready ;)
Davis Bennett
@d-v-b
Spikey!
joshua vogelstein
@jovo
@freeman-lab will you let us know if any 3D gets deposited into the datasets. i thought some data (not in the challenges perhaps) was already 3D? in the meantime, we'll work on supporting 2D images (we built the 3D image code based off your first light sheet data with misha)
Jeremy Freeman
@freeman-lab
ok the new Harvey lab data are posted, thanks to @mjlm and @Selmaan!
datasets 04.00 and 04.01
reran the "demo submissions" on them and everything went through fine
would be great if someone downloaded the actual data to double check formatting and stuff
@jovo there's no 3d data in the neurofinder datasets and we probably won't add any
from this point forward the datasets are more or less final, at least for this challenge
so people can start submitting algorithms!
joshua vogelstein
@jovo
@freeman-lab ok, thanks, good luck with the challenge!
Kyle
@kr-hansen
@freeman-lab I just downloaded the all.test zip folder. FYI, it doesn't include the 04.00.test or 04.01.test. I had to download those separately.
Jeremy Freeman
@freeman-lab
@kkcthans ah thanks for catching that! will fix
Jeremy Freeman
@freeman-lab
@/all @syncrostone has now posted a bunch of algorithm results, pretty interesting so far!
the "Suite2P" algorithm from @marius10p is doing the best, but isn't dramatically different from the NMF-based approaches
Davis Bennett
@d-v-b
great work! it would be helpful to see computational costs for each method, maybe starting simply with execution time?
Jasmine Stone
@syncrostone
@d-v-b Suite2P was about 20 min per dataset on a single machine, but it required a lot of memory (doesn't work on a laptop).
@d-v-b nmf and cnmf were run on the cluster and only took a couple minutes per dataset running on approximately 10 nodes.
Davis Bennett
@d-v-b
cool!
Jeremy Freeman
@freeman-lab
pretty sure Suite2P can be made much faster using GPUs, and the others will of course depend on the number of nodes, but should scale pretty well
Davis Bennett
@d-v-b
is it just an implementation detail that makes Suite2p GPU-dependent?
or is there something about the algorithm that crucially leverages GPUs
Jeremy Freeman
@freeman-lab
not totally sure
Jasmine Stone
@syncrostone
according to the documentation, it's about 3 times faster with GPU toggled on
Davis Bennett
@d-v-b
is that for source extraction or registration?
Jasmine Stone
@syncrostone
Unclear. I didn't run registration because neurofinder data has registration already run, and the double registration was giving weird results
Marius Pachitariu
@marius10p
The cell detection part in Suite2P is actually much faster than the rest of the pipeline, and we never bothered to accelerate it on a GPU.
I expect most of the run time you got was data reads and writes, as well as the SVD decomposition, which you should lower the parameters for, because the datasets are very short.
The number of clusters also will increase the run times of the cell detection part in Suite2P. The defaults assume several hundred (active) ROIs, and is robust in that range. These datasets have an order of magnitude fewer ROIs.
*active ROIs :)
Marius Pachitariu
@marius10p
also, cell detection in Suite2P is invariant to the duration of the recording (except for data reads/writes), but the NMF methods are at least linear. Here you have recordings that are literally 100 times shorter than realistic datasets.
Nicholas Sofroniew
@sofroniewn
@marius10p most the data sets are around 5 to 10 minutes long at 8 Hz (so 3000 frames) which is around 10x shorter than typical experiments reported in the literature, but which still represent a meaningful snapshot of neural activity. Personally I often gather such datasets as I'm exploring different parts of a field of view, so having algorithms that worked well under these conditions would be very useful to me.
In general I think it would be useful to know the minimum duration / amount of data required for algorithms to perform well and we can add longer data sets to try and facilitate this
Jeremy Freeman
@freeman-lab
tried to capture some of these thoughts here codeneuro/neurofinder#17
Jasmine Stone
@syncrostone
@marius10p Can you help me figure out what parameters to use on the expanded but downsampled datasets that we are working on getting up?
Marius Pachitariu
@marius10p
@syncrostone Sure thing, I'll add this information to one of my issues. With everything at 5-10 minutes, I think I'm beginning to like neurofinder much more!
@sofroniewn I thought they were all 1-2 minutes, but just some of them are. I do agree the algorithms have to be able to work on 10 minute recordings.
Nicholas Sofroniew
@sofroniewn
@marius10p great, downsampling the 30Hz ones and getting 5-10 min data sounds good
Jeremy Freeman
@freeman-lab
fyi everyone we replaced a couple of the datasets with longer versions, as discussed here codeneuro/neurofinder#17
all are now 7-15min and around 8Hz
versions linked to on the website should all be the latest