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Updated: May 27, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Compression-enabled interpretability of voxelwise encoding models.

Fatemeh Kamali1, Amir Abolfazl Suratgar1, Mohammadbagher Menhaj1

  • 1Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran.

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|February 19, 2025
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Summary
This summary is machine-generated.

Model compression makes brain activity prediction models more interpretable and stable. This technique improves accuracy in identifying visual stimuli and reveals larger, more centralized receptive fields in the visual pathway.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Machine Learning

Background:

  • Convolutional neural networks (CNNs) are effective for voxelwise modeling of brain activity from natural movies.
  • High parameter counts in CNNs hinder interpretability and stability of these predictive models.

Purpose of the Study:

  • To investigate if model compression can enhance the interpretability and stability of CNN-based voxelwise models.
  • To assess if compression techniques maintain or improve predictive accuracy.

Main Methods:

  • Applied filter/connection pruning and receptive field compression to CNN models.
  • Utilized principal component analysis for dimensionality reduction.
  • Evaluated model performance on a hold-out test set for visual stimulus identification.

Main Results:

  • Model compression improved the accuracy of visual stimulus identification.
  • Compressed models provided more stable interpretations of voxelwise pattern selectivity.
  • Receptive field compression resulted in larger, more centralized population receptive fields along the ventral visual pathway.

Conclusions:

  • Model compression is a viable strategy for developing more interpretable and stable voxelwise CNN models.
  • Compression techniques enhance the understanding of neural representations in the visual cortex.
  • Optimized receptive fields suggest shifts in visual processing along the ventral stream.