This is a tutorial for key data analysis steps used by neuroscientists - filtering, spike extraction, PCA, clustering, and spectral analysis


Updated Thu, 05 Aug 2021 12:36:09 +0000

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This tutorial shows how neuroscientists can use MATLAB to analyze neural signals. This includes best practices on
1. reading in diverse neural data
2. representing time-series data
3. filtering, smoothing, resampling data
4. clustering waveforms using PCA and Gaussian mixture models
5. frequency domain and time-frequency analyses
For those new to programming, several steps can be achieved with the MATLAB Signal Analyzer App - a click-and-point interface that generates code.
For mid-level programmers, this tutorial can be the starting point for more complicated signal processing and analysis workflows
For experts, this is a good tutorial for teaching the basics of neural signal analyses
To run this Live Script check if you have a MATLAB license from your school or university. If not, you can download a free trial for 30 days.
For a Japanese language version, check this entry

Cite As

Shubo Chakrabarti (2023). Electrophysiology-Tutorial-for-Neuroscience (https://github.com/MathWorks-Teaching-Resources/Electrophysiology-Tutorial-for-Neuroscience/releases/tag/v1.2), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2020b
Compatible with any release
Platform Compatibility
Windows macOS Linux

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Version Published Release Notes

See release notes for this release on GitHub: https://github.com/MathWorks-Teaching-Resources/Electrophysiology-Tutorial-for-Neuroscience/releases/tag/v1.2


See release notes for this release on GitHub: https://github.com/MathWorks-Teaching-Resources/Electrophysiology-Tutorial-for-Neuroscience/releases/tag/v1.1


To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.