作者: Jinseop S Kim , Matthew J Greene , Aleksandar Zlateski , Kisuk Lee , Mark Richardson
DOI: 10.1038/NATURE13240
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摘要: How does the mammalian retina detect motion? This classic problem in visual neuroscience has remained unsolved for 50 years. In search of clues, here we reconstruct Off-type starburst amacrine cells (SACs) and bipolar (BCs) serial electron microscopic images with help from EyeWire, an online community ‘citizen neuroscientists’. On basis quantitative analyses contact area branch depth retina, find evidence that one BC type prefers to wire a SAC dendrite near soma, whereas another far soma. The is known lag time response. A mathematical model shows how such ‘space–time wiring specificity’ could endow dendrites receptive fields are oriented space–time therefore respond selectively stimuli move outward direction Motion detection by thought rely largely on biophysics cell dendrites; machine learning used gamified crowdsourcing draw diagram involving identify plausible circuit mechanism selectivity; suggests similarities between insect vision. been intrinsic (SACs). Now Sebastian Seung colleagues have combined new machine-learning techniques crowd sourcing via EyeWire brain-mapping game redraw cells. Their results show selectivity established at presynaptic level — spatiotemporal inputs identifying neural circuits rather than properties SACs as key selectivity. brings mouse closer certain respects Reichardt motion detector characteristic