Connectome of a human foveal retina.

The fovea is a unique specialization of the primate retina and is a promising site for obtaining the first complete connectome of a human central nervous system (CNS) structure. Within the fovea, neural cells and circuits have been miniaturized and compressed during evolution to sample the visual image at highest spatial resolution and begin the neural processing that serves human form, color, and motion perception. Here we present a comprehensive analysis of a sample of human foveal retina using deep learning-based segmentation to reconstruct all cells and synaptic connections at nanoscale resolution. We classified ~3,000 cells into 51 distinct morphological types based on their structural features and connectivity patterns. Our observations reveal novel synaptic pathways absent in non-human primates, suggesting specialized circuits contribute uniquely to human trichromatic color vision. A biophysical model of the distinct connectomes made by gap junctions (electrical synapses) between short- (S) and medium-long- (ML) wavelength-sensitive cone photoreceptors, suggests chromatic interactions between S and ML cones prior to the first chemical synapse. Segmentation of retinal ganglion cells (RGCs) suggests the presence of only 11 visual pathways, with 5 high-density RGC pathways accounting for over 95% of foveal output to the brain: a dramatic contrast to the 40+ ganglion cell types recognized in mouse retina. Our connectomic analysis reveals distinctive features of human neural circuitry and demonstrates how AI-based computational approaches can advance understanding of human brain structure and function.