Dimensionality reduction in neuroscience

Description: This intensive 1.5-day course provides a comprehensive introduction to applying machine learning techniques for dimensionality reduction in neuroscience. Participants will explore a variety of powerful methods to analyze and visualize high-dimensional neural data, facilitating deeper insights into complex neural processes.

Next course time: October 23 to 24, 2024, 9 am to 5 pm and 9 am to 12:15 pm, respectively
Venue: online (Zoom-link will be provided via email)

duration: 8 h + 3.5 h
course held in: /
published: October 21, 2024
latest update: October 21, 2024 (10:03 am)

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Current announcements

On Wednesday, October 23, 2024, at 9 am, further course material will be available on a dedicated GitHub repository. Please come back to this page at that time and download the course material from the repository.

Course requirements

Basic knowledge of Python programming, usage of conda or pip for package management, usage of GitHub, and familiarity with Jupyter notebooks.
a laptop or desktop computer (no specific requirements except an internet connection) with a working Python and conda installation (e.g., minicondaꜛ or Anacondaꜛ)
Familiarity with fundamental concepts in machine learning and neuroscience is beneficial but not required.

Important note: Before the course starts, please make sure, that conda is working on your device. We can not provide installation or technical assistance during the course.

Trouble shootings: If you have problems with your computer and/or Anaconda, you can use an online Python compiler, e.g., Google Colab ꜛ. Please, ensure before the beginning of the course, that you can access the online compiler of your choice (e.g., create a Google account) and that you know how to operate it (again, during the course we can not provide installation or technical assistance).

Who should attend

Neuroscientists and researchers interested in applying machine learning techniques to their data.
Data scientists and machine learning practitioners looking to specialize in neuroscience applications.
Graduate students and professionals in related fields seeking practical skills in dimensionality reduction.

Course format

Lectures: Each topic will be introduced with a theoretical background and relevant mathematical foundations.
Hands-on Sessions: Practical exercises using Python and popular libraries (such as scikit-learn and PyTorch) to implement and experiment with each method. We will apply these techniques to neuroscience datasets.

Learning objectives

Understand the principles and applications of key dimensionality reduction techniques.
Gain hands-on experience in implementing these methods using Python.
Learn to interpret and visualize the results of dimensionality reduction in a neuroscience context.

Syllabus

The chapters of this course will be available on Wednesday, October 23, 2024, at 9 am (CEST). Please come back to this page at that time to access the course material.

Acknowledgements

In order to keep the lecture material open and free, the use of copyright-protected material has been avoided. Instead, freely accessible sources are embedded and quoted. The organizer of this lecture is not the author of these sources and is not responsible for their content. The organizer uses the embedded sources according to fair principles for educational purpose.

The organizer of this lecture is not affiliated with the authors of the content in the provided external links, and bears no responsibility for their content. These links are used solely for educational purposes.

Past courses

This course material is under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (CC BY-NC-SA 4.0).