Divisive normalization unifies disparate response signatures throughout the human visual hierarchy

Marco Aqil*, Tomas Knapen, Serge O. Dumoulin

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Neural processing is hypothesized to apply the same mathematical operations in a variety of contexts, implementing so-called canonical neural computations. Divisive normalization (DN) is considered a prime candidate for a canonical computation. Here, we propose a population receptive field (pRF) model based on DN and evaluate it using ultra-high-field functional MRI (fMRI). The DN model parsimoniously captures seemingly disparate response signatures with a single computation, superseding existing pRF models in both performance and biological plausibility. We observe systematic variations in specific DN model parameters across the visual hierarchy and show how they relate to differences in response modulation and visuospatial information integration. The DN model delivers a unifying framework for visuospatial responses throughout the human visual hierarchy and provides insights into its underlying information-encoding computations. These findings extend the role of DN as a canonical computation to neuronal populations throughout the human visual hierarchy.

Original languageEnglish
Article numbere2108713118
Pages (from-to)1-10
Number of pages10
JournalProceedings of the National Academy of Sciences of the United States of America
Volume118
Issue number46
DOIs
Publication statusPublished - 16 Nov 2021

Bibliographical note

Funding Information:
ACKNOWLEDGMENTS. This research was supported by Dutch Research Council Vici Grant 016.Vici.185.050 (to S.O.D.).

Publisher Copyright:
© 2021 National Academy of Sciences. All rights reserved.

Keywords

  • Divisive normalization
  • Human visual cortex
  • Information-encoding models
  • Population receptive fields
  • Ultra-high-field fMRI

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