This toolbox easily and automatically processes Neuropixels data using spikeinterface
This toolbox will:
- Automatically generate a config file to keep track of experimental parameters
- Extract and save the sync signal sent from the behavioural task
- For Neuropixels data there are two routes:
1)
- Estimate the probe drift over time (Boussard et al. 2021, implemented in spikeinterface)
- Perform drift registration and spike sort with Kilosort 2.0
- Estimate and perform drift with Kilosort 4.0 and spike sort with Kilosort 4.0
Many processing steps require a CUDA capable GPU.
- For Neuropixel data, a GPU with at least 10GB of onboard RAM is recommended
Clone a copy of the repository on your local machine (for example, in the home directory)
git clone https://github.com/JonathanAMichaels/PixelProcessingPipeline.git
Install spikeinterface from https://github.com/SpikeInterface/spikeinterface
Install Kilosort 4 from https://github.com/MouseLand/Kilosort
Organize each experiment into one directory with a Neuropixel folder inside (e.g. 041422_g0), and any kinarm files.
The first time you process an experiment, call
python pipeline.py -f "/path/to/sessionYYYYMMDD"
This will generate a config.yaml file in that directory with all the relevant parameters for that experiment generated automatically. Open that file with any text editor and add any session specific information to the Session parameter section. For example, for Neuropixels data you need to specify the brain area of each electrode and the recording coordinates.
To run a sort on the Neuropixel data, run:
python pipeline.py -f "/path/to/sessionYYYYMMDD" -neuro_sort
If the config.yaml is correct, you can run the pipeline with both steps (registration and sorting), for example
python pipeline.py -f "/path/to/sessionYYYYMMDD" -full
Alternatively, you can call any combination of:
-neuro_sort
-lfp_extract
This code does not currently process .kinarm files or combine behavioural information with synced neural data. This may be added at a later date.