Track: Extracellular Electrophysiology

Alongside the development of high-density recording probes (e.g. Neuropixels), the range and complexity of extracellular electrophysiology processing methods has greatly increased in recent years. In this course, we will cover the theory and practical implementation of the full processing pipeline, including preprocessing, sorting and quality control.

We will use SpikeInterface to implement the pipeline, with manual curation in the SpikeInterface GUI. We will explore modern toolkits for sorting quality assessment (Bombcell), unit matching (UnitMatch) and downstream analysis (pynapple).

Target audience

This course is suitable for researchers and students who are acquiring, or planning to acquire electrophysiology data and want to learn how to build a processing pipeline and understand the underlying theory.

This course is focused on analysing high-density recordings (e.g. Neuropixels) and would be useful for those collecting large datasets and unsure how to process them.

Course overview

Core workshop (Monday - Wednesday)

We will cover the following topics during the first three days: Introduction

We will begin with a high-level overview of extracellular electrophysiology data, including:

• Probe layouts and channel maps, timeseries sampling and accessing the associated metadata on the probe

• Visualising the probe and acquired data in SpikeInterface

• Implementing a simple pipeline in SpikeInterface, including preprocessing (phase shift, filtering, common median referencing), sorting (e.g. kilosort4) and computing quality metrics

Preprocessing

We will extend the initial, simple pipeline by exploring:

• Advanced preprocessing methods (IBL tools for assessing raw data quality, DREDGE motion correction)

• The theory behind the applied preprocessing steps

• Concatenating recordings for multi-session studies

Sorting

In this session, we will run multiple sorters (e.g. kilosort4, SpyKING CIRCUS, Mountainsort) and compare the outputs in SpikeInterface. We will also:

• Discuss the inner workings of a sorter in detail

• Cover unit matching with UnitMatch for tracking putative neurons over multiple sessions

Assessing Sorting Quality

In this session, we will cover how to assess the quality of the sorting outputs:

• Introduction to the different types of quality metrics

• Computing quality metrics in SpikeInterface and Bombcell

• Assessing the sorter output in the SpikeInterface GUI

• Creating and applying models which perform automated curation with UnitRefine

Afternoon: Analysing Outputs

In the final session, we will focus on combining spike sorting outputs with behavioural events for analysis. This will include time alignment between electrophysiology and behavioural events, and using pynapple to generate outputs (e.g. peristimulus time histograms).

Collaboration days (Thursday - Friday)

The final two days are dedicated to collaboration. We will join forces with participants from the BrainGlobe track to work together on participant-led projects.

• Skill building: we’ll start with a practical workshop on Git and GitHub to equip everyone with the necessary skills for collaborative coding.

• Project-based work: participants will self-organise into small teams to tackle projects hands-on. Coding is not a requirement; any idea that benefits from collaboration with other attendees is welcome. Potential project ideas include, but are not limited to:

  • Apply a tool: use what you’ve learnt to analyse a new dataset (your own or a public one).

  • Give feedback: report bugs and suggest features by raising issues on relevant open-source tools.

  • Make a contribution: submit a pull request to an open-source repository.

  • Collaborative writing: draft a white paper, blog post, or documentation together.

  • Prototype an idea: experiment with a new analysis or method.

• Presentation: teams will have the opportunity to share their progress and outcomes on the final afternoon.

Confirmed Instructors

• Joseph Ziminski

• Chris Halcrow

Prerequisites

The only prerequisite is a basic knowledge of programming in Python, and the scientific Python ecosystem. For those without this background, the preparatory month will equip you with all the skills needed to make the most of this course.

Hardware

You will need to bring your own laptop with Python installed. We will provide a small test dataset, so any fairly recent laptop will be sufficient. A GPU is not required.

Data

Sample data will be provided, but if you have any of your own extracellular electrophysiology data, please bring it with you.