title: The Gaussian derivative model for spatial-temporal vision: II. Cortical data.
abstract: Receptive fields of simple cells in the primate visual cortex were well fit in the space and time domains by the Gaussian Derivative (GD) model for spatio-temporal vision. All 23 fields in the data sample could be fit by one equation. varying only a single shape number and nine geometric transformation parameters. A difference-of-offset-Gaussians (DOOG) mechanism for the GD model also fit the data well. Other models tested did not fit the data as well as or as succinctly, or failed to converge on a unique solution, indicating over-parameterization. An efficient computational algorithm was found for the GD model which produced robust estimates of the direction and speed of moving objects in real scenes.
authors.affiliation: Harmony/Human Factors Group, General Motors Engineering, Warren, Michigan 48090-9010, USA. richard.a.young@gm.com
authors.forename: R A
authors.initials: RA
authors.lastname: Young
authors.forename: R M
authors.initials: RM
authors.lastname: Lesperance
mesh_terms: D000818:Animals; D006801:Humans; D008960:Models, Psychological; D009039:Motion Perception / Q000502:physiology*; D016011:Normal Distribution; D013028:Space Perception / Q000502:physiology*; D014793:Visual Cortex / Q000502:physiology*