Receptive fields of midget ganglion cells and parvocellular lateral geniculate nucleus (LGN) neurons show coloropponent responses because they receive antagonistic input from the middle-and long-wavelength sensitive cones. It has been controversial as to whether this opponency can derive from random connectivity; if receptive field centers of cells near the fovea are cone-specific due to midget morphology, this would confer some degree of color opponency even with random cone input to the surround. A simple test of this mixed surround hypothesis is to compare spatial frequency tuning curves for luminance gratings and gratings isolating cone input to the receptive field center. If tuning curves for luminance gratings were bandpass, then with the mixed surround hypothesis tuning curves for gratings isolating the receptive field center cone class should also be bandpass, but to a lesser extent than for luminance. Tuning curves for luminance, chromatic, and cone-isolating gratings were measured in macaque retinal ganglion cells and LGN cells. We defined and measured a bandpass index to compare luminance and center cone-isolating tuning curves. Midget retinal ganglion cells and parvocellular LGN cells had bandpass indices between 0.1 and 1 with luminance gratings, but the index was usually near 1 (meaning low-pass tuning) when the receptive field center cone class alone was modulated. This is strong evidence for a considerable degree of cone-specific input to the surround. A fraction of midget and parvocellular cells showed evidence of incomplete specificity. Fitting the data with receptive field models revealed considerable intercell variability, with indications in some cells of a more complex receptive structure than a simple difference of Gaussians model.
|Original language||English (US)|
|Journal||Journal of the Optical Society of America A: Optics and Image Science, and Vision|
|State||Published - Feb 1 2012|
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Computer Vision and Pattern Recognition