I have added the ‘gocue’ and ‘feedback_time’, as well as ‘feedback_type’ (positive or negative). ITI is always the first 500ms (or 50 bins) of the binned data. Note the feedback_time is always a 10-30ms after the response, but the response has variable delay from the ‘gocue’, up to a few hundred ms or more if the animal makes a ‘nogo’ response.
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I have added mouse_name and date_exp as fields for each session (out of the 39).
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I added the trough_to_peak times of each neuron’s spike. I also have the full waveforms, and I don’t want to add them to the files everyone uses. If it’s ok, I will put them into separate files on OSF for you to download?
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That’s perfect, thanks!
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I have now added the PCA-compressed spike waveforms directly to the data files. More information on how to load them in the data loader notebook!
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I have now added the passive trials with the same structure as the rest of the dataset, but with '_passive' appended to each relevant field. See the data loader notebook for more information.
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This is wonderful. Thank you Marius
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Hi everyone, does anyone know what ‘root’ is?
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Hi @pachitarium,
I have just started doing some temporal analysis on the data, and I am wondering if there is a easy way to get access to the “un-binned” spike data? As it is better for the analysis of temporal structure.
Any suggestions how I can go about to access the spike trains without binning?
Cheers!
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Hi @pachitarium,
I’m a bit confused about how the ITI could be always the first 500 ms of binned data. Since the initiation of the trial is dependent on when the mouse holds the wheel still, wouldn’t it be variable across trials? Is there data for the interval between when the trial ends and when the mouse begins a new one? Thanks!
Hi,
I just downloaded the spike data from the original link by Nick. I have written some code to import the data, although I have written it specifically for my purpose, you should be able to modify it with ease to suit your needs ^.^
You can find the code here.
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Hi @debbh,
Thanks so much this. I had also just finished downloading the data. Your code will definitely help to make sense of the huge directory structure in the full dataset.
Also our projects seem to be in a similar vein! 
Thank you for sharing.
Depends how you define the ITI. If you want to know the random duration the mouse had to hold the wheel still, I am afraid that data is not available in the repo. However, the ITI was almost always larger than 500ms, sometimes substantially so, and therefore most of the 500ms before a stimulus is ITI. If you want to know when the previous trial ends, I can add that, but I think I have to define it relative to reward delivery.
This notebook loads the data, but it doesn’t align it to taks events, which requires carefully following the instructions on the dataset wiki. I can also provide the script I used to make the NMA dataset, if it’s useful.
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Hello! Regarding the behavioural measures of this dataset, our group has two questions:
- what exactly is the “wheel” measure (units? what would position zero refer to?).
- how were the ‘reaction times’ in Fig 1d calculated (threshold? time zero?).
Many thanks in advance
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I can probably get you the spike times divided into trials and referenced relative to stim onset if that’s useful, just let me know.
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- Multiply by 0.135 to get into mm. This is the distance the mouse turned the wheel in that particular 10ms timebin. Zero means the wheel is still for 10ms.
- The reaction times in the paper are calculated with a fairly complex procedure (see methods) that tries to determine the very earliest movement while being robust to noise. They were not provided by Nick, but I think I will add those myself since they’d be useful to others too.
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Could you please include the x-y coordinates of eyes position tracked during the experiment?
Hi @pachitarium,
That would be very ideal and will save us a lot of preprocessing steps. Thank you so much in advance for this!