Abstract
Color perception relies on comparisons between adjacent lights, but how the brain performs these comparisons is poorly understood. To elucidate the underlying mechanisms, we recorded spiking responses of individual V1 neurons in macaque monkeys to pairs of stimuli within the classical receptive field (RF). We estimated the spatial-chromatic RF of each neuron and then presented customized colored edges using a closed-loop technique. We found that many double-opponent (DO) cells, which have spatially and chromatically opponent RFs, responded to chromatic contrast as a weighted sum, akin to how other V1 neurons responded to luminance contrast. Yet other neurons integrated chromatic signals nonlinearly, confirming that linear signal integration is not an obligate property of V1 neurons. The functional similarity of cone-opponent DO cells and cone non-opponent simple cells suggests that these two groups may share a common underlying circuitry, promotes the construction of image-computable models for full-color image representation, and sheds new light on V1 complex cells.
Highlights
53 54 Color depends on the spectral composition and spatial organization of lights (Brown & MacLeod, 1997; Kraft & Brainard, 1999; Monnier & Shevell, 2003; Shevell & Monnier, 2005; Xian & Shevell, 2004)
We found that many DO cells responded linearly to differences in cone-opponent signals across their receptive field (RF), in quantitive similarity to how other V1 neurons responded to spatial differences in luminance
Many V1 neurons that are driven strongly by modulations of the S-cones are poorly described by a linear model (Conway & Livingstone, 2006b; Horwitz, Chichilnisky, & Albright, 2005). Consistent with this observation, we found that the absolute value of the normalized S-cone weight derived from the hyperpixel spike-triggered average (STA) was correlated with isoresponse non-linearity index (NLI) (r = 0.23, p = 0.02, Spearman’s correlation; Figure 6)
Summary
18 19 We thank Yasmine El-Shamayleh, Fred Rieke, Greg Field and Jacob Yates for comments on the manuscript. NIH/ORIP grant P51OD010425, and NEI Center Core Grant for Vision Research P30 EY01730 to the University of Washington and R90 DA033461 (Training Program in Neural Computation and Engineering) to Abhishek De. DECLARATION OF INTERESTS The authors declare no competing interests
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