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Related Concept Videos

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Net2Brain: a toolbox to compare artificial vision models with human brain responses.

Domenic Bersch1,2, Martina G Vilas1,3, Sari Saba-Sadiya1

  • 1Department of Computer Science, Goethe Universität, Frankfurt am Main, Germany.

Frontiers in Neuroinformatics
|May 21, 2025
PubMed
Summary
This summary is machine-generated.

Net2Brain simplifies comparing artificial neural networks and brain data. This Python toolbox offers extensive models and datasets for robust cognitive neuroscience research.

Keywords:
artificial intelligence in neurosciencecognitive neurosciencedeep neural networksmultimodal neural modelsneuroimaging data analysistoolbox

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Deep neural networks (DNNs) and neuroscientific analyses are increasingly integrated.
  • Comparing representational spaces between DNNs and brain data presents challenges due to model variety and neuroimaging data demands.

Purpose of the Study:

  • Introduce Net2Brain, a Python toolbox to bridge DNNs and neuroscience.
  • Facilitate end-to-end analysis of artificial and biological neural representations.

Main Methods:

  • Net2Brain provides access to over 600 DNNs across various modalities.
  • Features streamlined API for neuroscience datasets (e.g., NSD, THINGS).
  • Supports representational similarity analysis (RSA), linear encoding, and advanced techniques.

Main Results:

  • Enables feature extraction, evaluation, and visualization for DNNs and brain data.
  • Integrates with existing open-source libraries for enhanced interoperability.
  • Simplifies model selection, data processing, and analysis.

Conclusions:

  • Net2Brain empowers researchers with a flexible and reproducible pipeline.
  • Enhances the investigation of relationships between artificial and biological neural representations.
  • Promotes robust and collaborative cognitive neuroscience research.