A Modeling Study of the Emergence of Eye Position Gain Fields Modulating the Responses of Visual Neurons in the Brain

Navarro, Daniel de Moraes; Smithson, Hannah E.; Stringer, Simon M.

doi:10.3389/fncir.2020.00030

Cited by 1 publication

(2 citation statements)

References 34 publications

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“…At the level of single neurons, EGFs have classically been described as planar (i.e., two-dimensional planes) 4 . However, they have also been described as various other shapes 30 , 31 to account for observed spatial non-monotonic features in EGFs 15 , 32 . Here, we opted to model pEGF at the level of single voxels with an isotropic two-dimensional gaussian described with four parameters: center (x, y), standard deviation and amplitude.…”

Section: Resultsmentioning

confidence: 99%

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Topographic organization of eye-position dependent gain fields in human visual cortex

2022

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The ability to move has introduced animals with the problem of sensory ambiguity: the position of an external stimulus could change over time because the stimulus moved, or because the animal moved its receptors. This ambiguity can be resolved with a change in neural response gain as a function of receptor orientation. Here, we developed an encoding model to capture gain modulation of visual responses in high field (7 T) fMRI data. We characterized population eye-position dependent gain fields (pEGF). The information contained in the pEGFs allowed us to reconstruct eye positions over time across the visual hierarchy. We discovered a systematic distribution of pEGF centers: pEGF centers shift from contra- to ipsilateral following pRF eccentricity. Such a topographical organization suggests that signals beyond pure retinotopy are accessible early in the visual hierarchy, providing the potential to solve sensory ambiguity and optimize sensory processing information for functionally relevant behavior.

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Section: Resultsmentioning

confidence: 99%

“…Second, it can account for spatial non-monotonicities in the EGF. Third, at the same time it can approximate planar shapes, in the case of an eccentric 2D gaussian with a (relatively) wide standard deviation 31 . In our modeling strategy we also constrained the pEGFs to be larger than a voxels’ pRF 12 .…”

Section: Resultsmentioning

confidence: 99%