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Follow Up and possible Bug concerning: Multi-class conditions and previous feature request #1 #8
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PS: Your documentation seems to be broken: |
Hey @JensBlack, thanks for your comments! A few points below
beware that this will not balance internal classes (e.g. 0 and 1 might have a different number of bins within the 'sleep' class).
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The docs should be fixed - check it out! |
Thank you for the wholesome answer. I will try this out! |
Hi @lposani,
I really enjoyed going through your material and trying out your package.
Running it on a test case of my data, I stumbled across a previous post #1 where you clarified that the current (past?) version is not capable of handling multiple classes in the conditions. However, if I put in multiple classes, the decoding runs without any issues and returns a result.
Am I assuming that this is not the intended behavior? Or has there been an update that now integrates multi-class conditions?
Code-snippet:
where:
ca_z
is z-scored calcium imaging traces of N neurons across time.sleep_labels
is a 1D array of sleep/awake stages including 4 classes [0,1,2,3]Running the decoding, gives me the following output:
Further questions:
The underlying assumption is that sleep stages are encoded in my neuronal data (or not).
With the package as is, do I need to binarize my condition and then filter my session data in order to discriminate for major classes (awake vs sleep) and minor classes (e.g., NREM vs REM sleep)?
As there is no trial structure in my data, because the animal is freely moving and not stimulated, is the package actually capable to investigate my use case?
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