A High-Dimensional View of Neuroscience

Tutorial session at Cognitive Computational Neuroscience 2023

Authors
Affiliations

Raj Magesh Gauthaman

Johns Hopkins University

Florentin Guth

École normale supérieure

Atlas Kazemian

Johns Hopkins University

Zirui Chen

Johns Hopkins University

Michael Bonner

Johns Hopkins University

Published

August 26, 2023

Modified

March 10, 2024

Motivation

Advances in technology enable us to record neural responses to many thousands of stimuli from a huge number of channels (e.g. fMRI in humans, two-photon imaging in mice, neuropixel probes in monkeys). Given the unprecedented scale of these data – collected with incredible effort at enormous expense – what computational tools can we use to study neural representations in high dimensions? What theoretical insights can we gain about the nature of neural representations from large-scale datasets?

Welcome!

This site contains material for a tutorial presented at the conference on Cognitive Computational Neuroscience 2023.

Don’t miss the tutorial!
Where
East Schools
When
Saturday, August 26, 2023 @ 10:45 - 12:30
Run the tutorial interactively – or just follow along on the website!

Each section is a computational notebook that can be run interactively on Google Colab or viewed rendered on this site – just follow the links below!

Section Read Interact Download
Introducing PCA website Colab download
Exploring neural data website Colab download
Dealing with noise website Colab download
Comparing representations website Colab download
Analyzing neural networks website Colab download

Create a Python virtual environment with Python >=3.10.12 to run the notebooks. The required dependencies will be automatically installed when you run the first cell of each notebook.

Notice a typo? Have any feedback?

Use the Report an issue button on the sidebar of each page to contact us. Feel free to suggest edits by using the Edit this page button too!

Back to top

Acknowledgments

Thanks to the Natural Scene Dataset team for permission to use it for this tutorial and to the Open Science Foundation for hosting the data files.

Reuse

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation

BibTeX citation:
@online{gauthaman2023,
  author = {Gauthaman, Raj Magesh and Guth, Florentin and Kazemian,
    Atlas and Chen, Zirui and Bonner, Michael},
  title = {A {High-Dimensional} {View} of {Neuroscience}},
  date = {2023-08-26},
  url = {https://BonnerLab.github.io/ccn-tutorial//},
  langid = {en-US},
  abstract = {Advances in technology enable us to record neural
    responses to many thousands of stimuli from a huge number of
    channels (e.g. fMRI in humans, two-photon imaging in mice,
    neuropixel probes in monkeys). Given the unprecedented scale of
    these data -\/- collected with incredible effort at enormous expense
    -\/- what computational tools can we use to study neural
    representations in high dimensions? What theoretical insights can we
    gain about the nature of neural representations from large-scale
    datasets?}
}
For attribution, please cite this work as:
Gauthaman, Raj Magesh, Florentin Guth, Atlas Kazemian, Zirui Chen, and Michael Bonner. 2023. “A High-Dimensional View of Neuroscience.” August 26, 2023. https://BonnerLab.github.io/ccn-tutorial//.