Tutorial Notebooks

There are several paths one can take to these notebooks. The notebooks have two-digits in their names, the first of which indicates its 'batch', as described in the categories below.

0. Intro

Everyone should complete the Setup and Insert Data notebooks. The Concepts notebook offers additional information that will help users understand the data structure and how to interact with it.

Data Sync is an optional additional tool for collaborators that want to share analysis files.

The Merge Tables notebook explains details on a pipeline versioning technique unique to Spyglass. This is important for understanding the later notebooks.

The Export notebook shows how to export data from the database.

1. Spike Sorting Pipeline

This series of notebooks covers the process of spike sorting, from automated spike sorting to optional manual curation of the output of the automated sorting.

Spikesorting results from any pipeline can then be organized and tracked using tools in Spikesorting Analysis.

2. Position Pipeline

This series of notebooks covers tracking the position(s) of the animal. The user can employ two different methods:

1. The simple Trodes methods of tracking LEDs on the animal's headstage
2. DLC (DeepLabCut) which uses a neural network to track the animal's body parts.

Either case can be followed by the Linearization notebook if the user wants to linearize the position data for later use.

3. LFP Pipeline

This series of notebooks covers the process of LFP analysis. The LFP covers the extraction of the LFP in specific bands from the raw data. The Theta notebook shows specifically how to extract the theta band power and phase from the LFP data. Finally the Ripple Detection notebook shows how to detect ripples in the LFP data.

4. Decoding Pipeline

This series of notebooks covers the process of decoding the position of the animal from spiking data. It relies on the position data from the Position pipeline and the output of spike sorting from the Spike Sorting pipeline. Decoding can be from sorted or from unsorted data using spike waveform features (so-called clusterless decoding).

The first notebook (Extracting Clusterless Waveform Features) in this series shows how to retrieve the spike waveform features used for clusterless decoding.

The second notebook (Clusterless Decoding) shows a detailed example of how to decode the position of the animal from the spike waveform features. The third notebook (Decoding) shows how to decode the position of the animal from the sorted spikes.

Developer note

The py_scripts directory contains the same notebook data in .py form to facilitate GitHub PR reviews. To update them, run the following from the root Spyglass directory

pip install jupytext
jupytext --to py notebooks/*ipynb
mv notebooks/*py notebooks/py_scripts
black .

Unfortunately, jupytext-generated py script are not black-compliant by default.

You can ensure black compliance with the pre-commit hook by running

pip install pre-commit

This will run black whenever you commit changes to the repository.