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Wei-Ming Huang, Hsiang-Yu Wu, Yu-Cheng Pei, Chun-I Yeh; Receptive Field Structures of Color-responsive Neurons in Macaque Monkey V1. Journal of Vision 2017;17(10):125. doi: 10.1167/17.10.125.
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© ARVO (1962-2015); The Authors (2016-present)
Visual receptive fields have been studied as a way to understand the properties of color- and luminance-responsive neurons in the primary visual cortex (V1). In macaque monkey V1, many neurons responding to color are highly selective for orientation and spatial frequency (Johnson et al., 2001; Friedman et al., 2003). One would predict that the receptive field structures of color-responsive neurons should consist of multiple elongated sub-regions (like simple cells). However, previous studies had shown mixed results: some found simple-cell-like receptive fields by using dense noise (Horwitz et al., 2007; Johnson et al., 2008), whereas others found receptive fields that were blub-like and less elongated when using sparse noise (Conway and Livingstone, 2006). Here we measured receptive fields of V1 color-responsive neurons with three different stimulus ensembles: Hartley gratings, binary white noise, and binary sparse noise. All three stimulus ensembles consisted of equiluminance colors of red and green representing different cone weights. Receptive fields were estimated by reverse correlation and fitted with the 2-D Gabor function. We studied 54 V1 units and found that Hartley maps tended to have higher aspect ratios (p=0.03) and larger numbers of subregions (p=0.02) than white-noise maps (Friedman's test). There was a negative correlation between the aspect ratio of the map and the circular variance measured with drifting gratings (Hartley gratings: r=-0.28, p=0.04; white noise: r=-0.30, p=0.04; Spearman's rank correlation). Similar to previous findings, the distribution of circular variances for color-responsive neurons was comparable with that for luminance-responsive neurons (Leventhal et al., 1995; Ringach et al., 2002). In summary, the receptive field of color-responsive neurons may change accordingly with different stimulus ensembles. For neurons that are well tuned for orientation, the tuning properties can be predicted by their receptive field structures.
Meeting abstract presented at VSS 2017
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