Framework for analyzing single cells

CellExplorer is a graphical user interface (GUI), a standardized processing module and data structure for exploring and classifying single cells acquired using extracellular electrodes.

Get started now View code on GitHub



The large diversity of cell-types of the brain, provides the means by which circuits perform complex operations. Understanding such diversity is one of the key challenges of modern neuroscience. Neurons have many unique electrophysiological and behavioral features from which parallel cell-type classification can be inferred.

To address this, we built CellExplorer, a framework for analyzing and characterizing single cells recorded using extracellular electrodes. It can be separated into three components: a standardized yet flexible data structure, a single yet extensive processing module, and a powerful graphical interface. Through the processing module, a high dimensional representation is built from electrophysiological and functional features including the spike waveform, spiking statistics, monosynaptic connections, and behavioral spiking dynamics. The user-friendly interactive graphical interface allows for classification and exploration of those features, through a rich set of built-in plots, interaction modes, cell grouping, and filters. Powerful figures can be created for publications. Opto-tagged cells and public access to reference data have been incorporated to help you characterize your data better. The framework is built entirely in MATLAB making it fast and intuitive to implement and incorporate CellExplorer into your pipelines and analysis scripts. You can expand it with your metrics, plots, and opto-tagged data. The paper is now available on bioRxiv:

Data structure Processing module Graphical interface

Getting started

  1. Clone, fork, or download the repository (cloning or forking is recommended).
  2. Add the local repository to your MATLAB setpath.
  3. The pipeline uses two c-code files that must be compiled CCGHeart.c and FindInInterval.c (originally part of the FMA toolbox). Compiled versions are included for Windows and Mac. If you are using Linux you have to compile the scripts. In MATLAB, go to CellExplorer/calc_CellMetrics/mex/ and run these line:
    mex -O CCGHeart.c
    mex -O FindInInterval.c
  4. CellExplorer uses additional toolboxes, of which one MATLAB toolbox must be installed manually.
  5. That’s it! Now you can explore the software with below example data or try one of the tutorials.

Try CellExplorer with example data

There is an example dataset included in the repository for trying CellExplorer. Load the mat-file cell_metrics_batch.mat into MATLAB and type:


Tutorials for using the framework with your own data

We have created a few tutorials to get you started, covering the pipeline and the graphical interface. There is also a tutorial script: CellExplorer_Tutorial.m included with example code for running the pipeline and the GUI on your data.

View tutorials

Reporting bugs, enhancements or questions

Please use the GitHub issues system for reporting bugs, enhancement requests or general questions.

Citing CellExplorer in your research and publications

Peter Christian Petersen, György Buzsáki (2020). CellExplorer: a graphical user interface and standardized pipeline for visualizing and characterizing single neuron features. bioRxiv 2020.05.07.083436; doi: Download pdf.

Video demonstrating the user-friendly capabilities of CellExplorer

The video can be streamed on YouTube in 4K and is available for download (60MB). For best viewing experience on YouTube, select highest resolution and maximize the video.