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Divisive normalization unifies disparate response signatures throughout the human visual hierarchy
Marco Aqil1,2, Tomas Knapen3,2, Serge O Dumoulin3,2,4
1Spinoza Centre for Neuroimaging, 1105 BK Amsterdam, Netherlands; m.aqil@spinozacentre.nl.
Summary
Divisive normalization (DN) serves as a canonical neural computation, unifying visuospatial responses across the human visual hierarchy. This new population receptive field (pRF) model based on DN offers superior performance and biological plausibility compared to existing models.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Visual Neuroscience
Background:
- Neural processing may employ consistent mathematical operations, termed canonical neural computations.
- Divisive normalization (DN) is a leading candidate for such a canonical computation.
- Existing population receptive field (pRF) models do not fully capture the complexity of neural responses.
Purpose of the Study:
- To propose and evaluate a novel population receptive field (pRF) model based on divisive normalization (DN).
- To investigate the role of DN as a canonical computation across the human visual hierarchy.
- To understand how DN parameters relate to response modulation and information integration.
Main Methods:
- Development of a new pRF model incorporating DN.
- Evaluation of the DN-based pRF model using ultra-high-field functional MRI (fMRI) data.
- Analysis of systematic variations in DN model parameters across the visual hierarchy.
Main Results:
- The DN model parsimoniously explains diverse neural response signatures with a single computation.
- The DN model outperforms existing pRF models in both predictive performance and biological plausibility.
- Systematic variations in DN parameters correlate with differences in response modulation and visuospatial information integration.
Conclusions:
- The DN model provides a unifying framework for understanding visuospatial responses throughout the human visual hierarchy.
- DN acts as a canonical computation for neuronal populations across the visual system.
- This research offers insights into the information-encoding computations underlying neural processing.
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