Connectomic reconstruction of the inner plexiform layer in the mouse retina

• Published in: Nature (London) Vol. 500; no. 7461; pp. 168 - 174
• Main Authors: HELMSTAEDTER, Moritz, BRIGGMAN, Kevin L, TURAGA, Srinivas C, JAIN, Viren, SEBASTIAN SEUNG, H, DENK, Winfried
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group 08.08.2013
• Subjects: Amacrine Cells - cytology; Amacrine Cells - physiology; Animals; Biological and medical sciences; Cell Communication; Classification; Connectome; Eye and associated structures. Visual pathways and centers. Vision; Fundamental and applied biological sciences. Psychology; Image Processing, Computer-Assisted; Mice; Mice, Inbred C57BL; Microscopy; Microscopy, Electron; Models, Biological; Neural networks; Neuropil - physiology; Retina - cytology; Retina - physiology; Retinal Ganglion Cells - cytology; Retinal Ganglion Cells - physiology; Studies; Vertebrates: nervous system and sense organs; Cartography; Data analysis; High resolution; Rodentia; Exploration; Retina; Nervous system; Interneuron; Neuropile; Eye; Learning; Visual system; Vertebrata; Mammalia; Acquisition process; Mouse; Animal; Inner plexiform layer
• DOI: 10.1038/nature12346
• ISSN: 0028-0836

Comprehensive high-resolution structural maps are central to functional exploration and understanding in biology. For the nervous system, in which high resolution and large spatial extent are both needed, such maps are scarce as they challenge data acquisition and analysis capabilities. Here we present for the mouse inner plexiform layer--the main computational neuropil region in the mammalian retina--the dense reconstruction of 950 neurons and their mutual contacts. This was achieved by applying a combination of crowd-sourced manual annotation and machine-learning-based volume segmentation to serial block-face electron microscopy data. We characterize a new type of retinal bipolar interneuron and show that we can subdivide a known type based on connectivity. Circuit motifs that emerge from our data indicate a functional mechanism for a known cellular response in a ganglion cell that detects localized motion, and predict that another ganglion cell is motion sensitive.