Marco Aqil

Host: Serge Dumoulin
Group leader of the Spinozacentre.
e-mail: s.dumoulin@spinozacentre.nl
Computational Cognitive Neuroscience & Neuroimaging.

The Spinoza Speaker: Marco Aqil PhD candidate, Spinozacentre. Computational Cognitive Neuroscience & Neuroimaging.m.aqil@spinozacentre.nl

Title: Levels of vision: from computational models to psychedelics.

Abstract:
A goal of cognitive neuroscience is to provide accounts of brain functions in terms of information processing [1]. Vision has long been used as a beachhead for such approaches [2]: information-processing principles first identified in the visual system, such as receptive fields and divisive normalization, have later been observed in a variety of other sensory and cognitive domains [3]. Here, we formulated a visual-spatial population receptive field model based on divisive normalization, a candidate ‘canonical’ neural computation. We showed that the model unifies and outperforms existing models, and that it does so via local variations in its algorithmic parameters [4]. Next, we hypothesised that neurotransmitter systems might provide the biological-implementational substrate underlying the model’s algorithmicmodulations. We showed that model parameter estimates are related to density maps of different serotonin and GABA receptors [5]. Finally, in order to directly probe the role of neurotransmitter systems in human visual computations, we administered psilocybin, a serotonin receptor agonist. We showed that psilocybin systematically alters the model’sparameter relevant for suppression (center-surround configurations), as well as the bias in a relevant task (Ebbinghaus illusion). Our findings bring together computational vision with chemoarchitecture and neuropharmacology, providing new insights into the human visual system.

References
[1] Marr, D. (1982). Vision: A computational investigation into the human representation and processing of visual information. MIT press.
[2] Heeger, D. J., Behrmann, M., & Dinstein, I. (2017). Vision as a beachhead. Biological psychiatry, 81(10), 832-837.
[3]Carandini, M., & Heeger, D. J. (2012). Normalization as a canonical neural computation. Nature reviews neuroscience, 13(1), 51-62.
[4] Aqil, M., Knapen, T., & Dumoulin, S. O. (2021). Divisive normalization unifies disparate response signatures throughout the human visual hierarchy. Proceedings of the National Academy of Sciences, 118(46), e2108713118.
[5] Aqil, M., Knapen, T., & Dumoulin, S. O. (2024). Computational model links normalization to chemoarchitecture in the human visual system. Science advances, 10(1), eadj6102.