Task relevant autoencoding enhances machine learning for human neuroscience

Task relevant autoencoding enhances machine learning for human neuroscience

Abstract In human neuroscience, machine learning can help reveal lower-dimensional neural representations relevant to subjects’ behavior. However, state-of-the-art models typically require large datasets to train, and so are prone to overfitting on human neuroimaging data that often possess few samp...

Full description

Saved in:
Bibliographic Details
Main Authors: Seyedmehdi Orouji, Vincent Taschereau-Dumouchel, Aurelio Cortese, Brian Odegaard, Cody Cushing, Mouslim Cherkaoui, Mitsuo Kawato, Hakwan Lau, Megan A. K. Peters
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Human neuroscience
Machine learning
Dimensionality reduction
Task-relevant representation
fMRI
MVPA
Online Access:https://doi.org/10.1038/s41598-024-83867-6
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1038/s41598-024-83867-6

Similar Items