December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
Divisive normalization and the computational neuropharmacology of vision
Author Affiliations
  • Marco Aqil
    Spinoza Centre for Neuroimaging
  • Tomas Knapen
  • Serge Dumoulin
Journal of Vision December 2022, Vol.22, 3215. doi:
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      Marco Aqil, Tomas Knapen, Serge Dumoulin; Divisive normalization and the computational neuropharmacology of vision. Journal of Vision 2022;22(14):3215.

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      © ARVO (1962-2015); The Authors (2016-present)

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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 use a combination of state-of-the-art experiments (ultra-high-field functional MRI, PET) and mathematical methods (population receptive field [pRF] modeling) to investigate the role of divisive normalization (DN) as the canonical neural computation underlying visuospatial responses throughout the human visual hierarchy. We found that 1) a DN-pRF model explains seemingly unrelated response signatures, unifying and outperforming existing pRF models throughout the human visual hierarchy, and 2) specific model parameters modulate the presence of distinct nonlinear response properties (surround suppression and compressive spatial summation). Furthermore, we investigated the biophysical implementation underlying DN. Activation of specific neurotransmitter receptors is known to modulate responses to visual stimuli; hence, we hypothesized that specific neuropharmacological mechanisms may also underlie the operations of DN throughout the visual system. To test this hypothesis, we compared maps of DN pRF model parameters to the distribution of serotonin and GABA receptors obtained from PET imaging. We found highly significant correlations between receptor densities, in particular GABA and 5-HT1B, and DN model parameters. Our findings 1) extend the role of DN as a canonical computation to neuronal populations throughout the human visual hierarchy and 2) provide novel evidence for the role of neurotransmitter systems as the biological mechanism underlying neuromodulation of DN computations. We propose that these findings provide new insights into the canonical principles of information encoding in the cortex, as well as their biophysical implementation.


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