Divisive normalization unifies disparate response signatures throughout the human visual hierarchy

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 118; no. 46; pp. 1 - 10
• Main Authors: Aqil, Marco, Knapen, Tomas, Dumoulin, Serge O.
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 16.11.2021
• Subjects: Biological Sciences; Computation; Functional magnetic resonance imaging; Humans; Information processing; Magnetic Resonance Imaging - methods; Mathematical models; Models, Neurological; Neurons - physiology; Photic Stimulation - methods; Receptive field; Signatures; Visual Cortex - physiology; Visual observation; ultra-high-field fMRI; human visual cortex; population receptive fields; divisive normalization; information-encoding models
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.2108713118

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.