Abstract
Information integration occurs at every sensory level and some distinctions between levels seem arbitrary. For example, rattlesnakes have two separate visual systems, one for optical information transduced by photoreceptors and one for infrared stimuli transduced by heat-gated trigeminal neurons. This is traditionally a sensory integration problem and I’ve modeled it as a binding problem (Billock & Tsou, JCNS, 2014), but how is integration of visible and infrared light really different from integration of wavelength selective mechanisms in human color vision? This idea is explored by applying neural synchronization and amplification models (developed for visual/infrared integration in rattlesnake and visual/auditory interactions in cat) to understand broadening and amplification of spectral sensitivity in chromatic brightness and yellowness. Nonlinear interactions in these models amplify weak sensory signals much more than strong signals – in sensory integration this is the famous Principle of Inverse Enhancement. Chromatic Brightness: Chromatic brightness is broader in spectral sensitivity than luminance and is generally modeled as a nonlinear combination (e.g., a vector sum) of hue and luminance, but this is problematic on neural grounds. Alternatively, chromatic brightness spectral sensitivity modeled as a nonlinear amplification of luminance conforms better to sensory integration's Principle of Inverse Enhancement than do most sensory integration data! Yellowness: The spectral sensitivity of the yellow lobe of the y-b hue opponent channel is not a linear transformation of cone spectral sensitivities, but usually shows a nonlinear amplification of L-cone signals when M-cone signals are present, often manifesting as a strong spike of sensitivity near 570 nm. I modeled this amplification by substituting L+M- and M+L- neurons for the visual and auditory inputs in Billock & Tsou’s (JCNS, 2014) sensory integration model. The model creates a spectral sensitivity spike near 570 nm that is strongly dependent on stimulus intensity, a prediction suitable for psychophysical testing.
Meeting abstract presented at VSS 2015