Open AccessDivisive normalization unifies disparate response signatures throughout the human visual hierarchy
Author(s) -
Marco Aqil,
Tomas Knapen,
Serge O. Dumoulin
Publication year - 2021
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2108713118
Subject(s) - normalization (sociology) , hierarchy , computation , computer science , artificial intelligence , population , receptive field , visual field , visual processing , machine learning , pattern recognition (psychology) , neuroscience , psychology , algorithm , perception , demography , sociology , anthropology , economics , market economy
Significance A canonical neural computation is a mathematical operation applied by the brain in a wide variety of contexts and capable of explaining and unifying seemingly unrelated neural and perceptual phenomena. Here, we use a combination of state-of-the-art experiments (ultra-high-field functional MRI) and mathematical methods (population receptive field [pRF] modeling) to uniquely demonstrate the role of divisive normalization (DN) as the canonical neural computation underlying visuospatial responses throughout the human visual hierarchy. The DN pRF model provides a tool to investigate and interpret the computational processes underlying neural responses in human and animal recordings, but also in clinical and cognitive dimensions.