Resources

This section includes articles and videos providing background and technical detail on extracellular electrophysiology data preprocessing and analysis.

This section is by no means exhaustive, please feel free to get in contact to suggest additions to this page.

For pipeline building, we recommend SpikeInterface, an open source community toolkit for extracellular electrophysiology, for preprocessing and spike sorting. To begin building your pipeline, the SpikeInterface documentation is a good starting point.

General Introduction

Below are a selection of papers that give a history and overview of the extracellular electrophysiology landscape:

Steinmetz NA et al. (2018). Challenges and opportunities for large-scale electrophysiology with Neuropixels probes. Current Opinion in Neurobiology.

Buccino AP et al. (2022). Spike sorting: new trends and challenges of the era of high-density probes. Progress in Biomedical Engineering.

Rey HG et al. (2015). Past, present and future of spike sorting techniques. Brain Research Bulletin.

Carlson D et al. (2019). Continuing progress of spike sorting in the era of big data. Current Opinion in Neurobiology

Technical Introduction

This section includes more technical resources on the different stages of extracellular electrophysiology analysis.

A particularly useful resource is the Neuropixels course, with their videos published online (e.g. 2023). While these are targeted towards Neuropixels users, they are valuable resources for any researcher approaching electrophysiology preprocessing and analysis.

Preprocessing

The IBL white paper contains a clearly written overview of common preprocessing steps. Similarly, Bill Karsh’s guide on SpikeGLX preprocessing tools gives a useful overview.

de Cheveigné & Nelken (2019) provide a technical treatment of digital filtering, a key step in preprocessing and analysis.

Spike Sorting

This video on Spike Sorting with Christophe Pouzat, provides an excellent overview of the spike-sorting problem.

This article provides a more detailed introduction to spike sorting and associated quality metrics:

Hill DN et al, (2007). Spike Sorting. In Observed Brain Dynamics by P. P. Mitra and H. Bokil.

It is also recommended to check out the papers of existing spike sorting algorithms. A list of the main spike sorters can be found on the SpikeInterface website.

Quality Metrics and Manual Curation

Assessing the quality of spike-sorting is a key to producing high-quality data.

These two papers provide a nice introduction to quality metrics for assessing spike sorting outputs:

Hill DN et al. (2011). Quality Metrics to Accompany Spike Sorting of Extracellular Signals. Journal of Neuroscience.

Harris KD et al. (2016). Improving data quality in neuronal population recordings. Nature Neuroscience.

Phy is the most popular tool for performing manual curation of spike sorting results. A great guide by Steve Lenzi takes you through the key steps for manual curation.

More recently, advances in the automating curation has been made in the Bombcell package.

SpikeInterface also maintains a set of quality metrics, explained in detail in their documentation.

SpikeInterface

Visit the SpikeInterface GitHub and Documentation to get started.

Other Community Tools

Analysis

SpikeInterface is mainly focused on preprocessing, spike sorting and quality metrics. Pynapple, Elephant, and Nemos all provide useful toolboxes for analysing data post-sorting.

The SpikeForest project is an excellent resource for assessing the performance of different spike-sorting algorithms across probe types and brain regions.

Pipelines

The Allen spike-sorting pipeline

The IBL sorting pipeline

For working with NeuroPixels, Neuropixels Utils package (MATLAB) and NeuroPyxels (Python).