Direction and speed selectivity properties for spatio-temporal receptive fields according to the generalized Gaussian derivative model for visual receptive fields
Tony Lindeberg

TL;DR
This paper provides a theoretical analysis of the direction and speed selectivity of visual receptive fields modeled by generalized Gaussian derivatives, aligning with neurophysiological data and supporting motion processing hypotheses.
Contribution
It introduces a velocity-adapted affine Gaussian derivative model for visual receptive fields and characterizes their selectivity properties, bridging theoretical modeling with biological observations.
Findings
Model predicts velocity-tuned neurons with specific direction and speed sensitivities.
Results align with neurophysiological measurements of visual cortex neurons.
Supports the hypothesis of covariant simple cells under Galilean transformations.
Abstract
This paper gives an in-depth theoretical analysis of the direction and speed selectivity properties of idealized models of the spatio-temporal receptive fields of simple cells and complex cells, based on the generalized Gaussian derivative model for visual receptive fields. According to this theory, the receptive fields are modelled as velocity-adapted affine Gaussian derivatives for different image velocities and different degrees of elongation. By probing such idealized receptive field models of visual neurons to moving sine waves with different angular frequencies and image velocities, we characterize the computational models to a structurally similar probing method as is used for characterizing the direction and speed selective properties of biological neurons. By comparison to results of neurophysiological measurements of direction and speed selectivity for biological neurons in…
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Taxonomy
TopicsVisual perception and processing mechanisms · Neural dynamics and brain function · Neurobiology and Insect Physiology Research
