An image-computable, spatio-chromatic RF model of the midget RGC mosaic across the retina
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Accurate image-computable models of retinal ganglion cell (RGC) mosaics across the retina do not currently exist. Here, we deploy a novel computational framework which synthesizes mosaics of linear spatio-chromatic receptive fields (RFs) of ON midget RGCs (mRGCs) by integrating published anatomical, physiological, and optical quality measurements. We use the synthesized mRGC mosaics to simulate both in vivo and in vitro physiological experiments and demonstrate the model’s consistency with published data. The model enables computation of how visual performance is shaped by the representation of visual information provided by the linear spatiochromatic processing stage of midget RGCs. The developed computational framework carefully accounts for the effect of physiological optics on mRGC responses, enables comparison of in vivo and in vitro data, and allows exploration of how different assumptions about RF organization, such as selectivity for the type of cones pooled by the RF center mechanism, affect physiological responses and psychophysical performance. The open-source and freely available implementation provides a platform for understanding how the linear spatiochromatic receptive field representation of the mRGCs shapes visual performance, as well as a foundation for future work that incorporates response nonlinearities, temporal filtering, and extends to additional RGC mosaics.
Author summary
We present a comprehensive, image-computable model of the human midget retinal ganglion cell (mRGC) mosaic that integrates diverse anatomical, optical, and physiological data. A central challenge in retinal modeling is reconciling measurements from in vivo recordings, which are affected by the eye’s optics, and in vitro recordings, which are not. Our model overcomes this by explicitly separating the optical and post-receptoral stages of visual processing. The synthesis of the mRGC receptive fields is guided by multiple constraints, including cone and mRGC densities, macaque neurophysiology, and human optical quality. The resulting model validates well against both in vivo and in vitro data and provides a powerful tool for understanding how linear spatial pooling in the retina limits human visual performance across the visual field.