Dynamic brightness induction in V1: Analyzing simulated and empirically acquired fMRI data in a "common brain space" framework
Research output: Contribution to journal › Article › Academic › peer-review
Computational neuromodeling may help to further our understanding of how empirical neuroimaging findings are generated by underlying neural mechanisms. Here, we used a simple computational model that simulates early visual processing of brightness changes in a dynamic, illusory display. The model accurately predicted illusory brightness changes in a grey area of constant luminance induced by (and in anti-phase to) luminance changes in its surroundings. Moreover, we were able to directly compare these predictions with recently observed fMRI results on the same brightness illusion by projecting predicted activity from our model onto empirically investigated brain regions. This new approach in which generated network activity and measured neuroimaging data are interfaced in a common representational "brain space" can contribute to the integration of computational and experimental neuroscience.
- Brightness illusion, Large-scale neuromodeling, neuroimaging, Surface perception, PRIMARY VISUAL-CORTEX, MACAQUE STRIATE CORTEX, PERCEPTUAL FILLING-IN, RECURRENT NETWORK ARCHITECTURE, SPATIAL-FILTERING ACCOUNT, CYTOCHROME-OXIDASE BLOBS, RECEPTIVE-FIELDS, FUNCTIONAL-ORGANIZATION, INTRINSIC CONNECTIONS, 3-DIMENSIONAL FORM