Model used in the publication

[ZyHUpenn]

Hi, I really interested which model you used for voltage imaging data of mouse cortex layer and zebrafish. Because recently I was dealing with similar voltage imaging data, and I found some tiny irregular spikes that looks like noise on my baseline. Did you use bs1 or bs3, or other models?

[ZyHUpenn]

I do the average in cell's region and there seems to be many irregular spikes, then I was wondering if it is because the trainning set of the model are quite diffrent which may cause some noise being ignored.

[EOMMINHO]

Hi, I am not sure what the problem is only looking at the trace.
We used bs1 for the data that does not contain structural noise and used bs3 otherwise.
It might be better to try bs3 if denoising does not work correctly.

Did you use the pre-trained model we uploaded or trained the model on your own?

[ZyHUpenn]

I used pre-trained model and I remembered that is bs1.

[ZyHUpenn]

See comparing the denoised data in your publication, the curve is clean and smooth but for our data I directly applied SUPPORT and the baseline seeems incorrect.

[SteveJayH]

Here is model parameters which were used for mouse and zebrafish data in Fig. 4: Denoising population voltage imaging data. of our paper.
GDrive

Try that two parameters, and different imaging rate, spike property, noise level... between train / test data could hinder perfect denoising.

The best way is to train SUPPORT to your data. If these pretrain models also not work well and if you have any difficulties on training, let us know.

[ZyHUpenn]

I will try it, thank you very much!

[ZyHUpenn]

Sorry, seems the pre-trained model's parameter 'in-channels' is 61, the model parameter's 'in-channels' you shared is 16, shall I use 61 or change the layer's architecture?

[SteveJayH]

Ah, sorry for the inconvenience. That was typo, 61 is correct.

You can load both parameters like this. (I checked it now!)

[ZyHUpenn]

Yes, I already validated the model on your zebrafish data, it works well. Seems we need to train models with our data in order to get good results. Thank you very much for the help!

[ZyHUpenn]

Hi, can I ask what's the raw data you used in Fig.4a and 4d? I downloaded the mouse cortex data and found they are all single cell's images and for zebrafish's data seems they are brain's images while Fig 4d seems from the body. Besides, can I ask how many data you used in the training? I trained some of my data however the SNR's improvement is not obvious. Thanks for your time!

[EOMMINHO]

Hi, the data used in Fig.4a can be downloaded from (https://zenodo.org/record/4515768#.ZC0_DHZByUk), and the data used in Fig.4d can be downloaded from (https://figshare.com/articles/dataset/Voltage_imaging_in_zebrafish_spinal_cord_with_zArchon1/14153339).
We trained each model using a single video.

[ZyHUpenn]

Thank you very much! Can I ask how you train your video, like learning rate or other parameters? I trained with a 3000020096, filming rate = 1000 movie for 100 epochs for saving the time, the loss is around 0.1 and the loss decay is really unobvious, shall I increase my training epochs or modify some other parameters?

[EOMMINHO]

We trained the model with the default parameters uploaded on (https://github.com/NICALab/SUPPORT/blob/main/src/utils/util.py). If you found that the improvement of SNR is not obvious, I would like to recommend increasing the "bs_size" to [3, 3] or higher.

[ZyHUpenn]

I used bs_size [3,3] to train with my movie, seems there is not much improvement. The training loss is around 0.10. My movie is 200 * 96 * 30000, each frame looks like the image attached, is it the different space size or too much background in our movie that cause the problem? Do you think I need to modify some network architecture like change the kernel size or add some regularization?

[SteveJayH]

1. Is the attached frame denoised, or is it raw??

2. What does not much improvement mean in detail?

• Do you observe "noise" also in the denoised frame? (spatially)
• Or it is visually okay, but noise did not reduce in ROI trace? (temporally)

If the noise remains in the denoised frame, we usually increase the bs_size.

p.s., Is it possible to share the data and let us try processing it?
We are not sure just by looking at one frame of the data...
I think your data is not much different from the data we have processed... of course, based on current information.