Comments (4)
ahbarnett
commented on July 17, 2024
You could just make a custom version of:
https://github.com/magland/ml_ms4alg/blob/master/examples/ms4alg_pipelines.py
skipping filtering.
But then the simplest thing is simply concatenate your clips (extracted
spikes of equal durations), and send to ms4_alg anyway. You just have to
convert the firing times to your clip number, which is easy.
Jeremy may comment on where the ms4_alg script has the separate processor
steps.
…On Wed, Aug 12, 2020 at 10:41 AM LAcharya ***@***.***> wrote:
Hello!
I'm unsure if 'Issues' is the correct place for a question about correct
usage. I'd like to know how to use MountainSort with already extracted
spikes. I have spike times, and spike waveforms for spikes extracted from
all four channels of tetrodes. How do I skip the filtering, and spike
extraction steps?
Thanks!
Lavanya
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LAcharya
commented on July 17, 2024
But then the simplest thing is simply concatenate your clips (extracted
spikes of equal durations), and send to ms4_alg anyway. You just have to
convert the firing times to your clip number, which is easy.
I don't understand what you mean by converting firing times to clip number. Each extracted waveform has 48 data points and one firing timestamp, as determined by the spike extraction algorithm of the recording system. So, from what I understand, I can concatenate the spikes into a single vector, and arrange the data from the four channels of the tetrode into an mda array. And then feed this into the pipeline to be filtered, and for spikes to be extracted, as if it was a raw data stream?
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ahbarnett
commented on July 17, 2024
Yes, but what you'll get out of MS is firing times and labels. You'll need
to then convert the times back into your clip label, is all I meant.
…On Wed, Aug 12, 2020 at 11:33 AM LAcharya ***@***.***> wrote:
But then the simplest thing is simply concatenate your clips (extracted
spikes of equal durations), and send to ms4_alg anyway. You just have to
convert the firing times to your clip number, which is easy.
I don't understand what you mean by converting firing times to clip
number. Each extracted waveform has 48 data points and one firing
timestamp, as determined by the spike extraction algorithm of the recording
system. So, from what I understand, I can concatenate the spikes into a
single vector, and arrange the data from the four channels of the tetrode
into an mda array. And then feed this into the pipeline to be filtered, and
for spikes to be extracted, as if it was a raw data stream?
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Institute
| \ http://users.flatironinstitute.org/~ahb
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LAcharya
commented on July 17, 2024
Got it! So the MS output of firing times will assume that the first data point of the first clip is 0 ms, and will give me timestamps of detected spikes with respect to that. Thanks!
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Related Issues (20)
- difference between consolidate_clusters and merge_across_channels parameters HOT 3
- Error with bandpass_filter in MountainSort3 HOT 3
- error code 9 , computing cluster metrics
- error code 6, mountainsortalg.ms3alg -- Unexpected error putting together the clips in NeighborhoodSorter::sort()
- How to make a geom.csv file? HOT 4
- Data requirement of mountainsort HOT 2
- How can I change the config temp directory non-interactively? HOT 2
- How does Mountainsort handle coincident spikes from different neurons HOT 1
- Should whitened data be used to plot units after sorting
- pyms error HOT 16
- is this software suitable for online realtime signal analysis?
- detect_threshold
- Single channel sort not working HOT 1
- Can't finish the sorting process using ms4alg.sort HOT 1
- Index out of bounds error: multiprocessing/pool.py HOT 4
- Error just after installation using conda HOT 5
- Clip alignment for low sampling rate recordings [future development question] HOT 2
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