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The connectome of an insect brain
Michael Winding (Howard Hughes Medical Institute, MRC Laboratory of Molecular Biology), Benjamin D. Pedigo (Johns Hopkins University), Christopher L. Barnes (MRC Laboratory of Molecular Biology, University of Cambridge), Heather G. Patsolic (Johns Hopkins University), Youngser Park (Johns Hopkins University), Tom Kazimiers (Howard Hughes Medical Institute, Janelia Research Campus), Akira Fushiki (Howard Hughes Medical Institute, Janelia Research Campus), Ingrid V. Andrade (University of California, Los Angeles), Avinash Khandelwal (Howard Hughes Medical Institute, Janelia Research Campus), Javier Valdés-Alemán (Howard Hughes Medical Institute, Janelia Research Campus), Feng Li (Howard Hughes Medical Institute, Janelia Research Campus), Nadine Randel (MRC Laboratory of Molecular Biology, University of Cambridge), Elizabeth Barsotti (MRC Laboratory of Molecular Biology, University of Cambridge), Ana Correia (MRC Laboratory of Molecular Biology, University of Cambridge), Richard Doty Fetter (Howard Hughes Medical Institute, Janelia Research Campus), Volker Hartenstein (University of California, Los Angeles), Carey E. Priebe (Johns Hopkins University), Joshua T Vogelstein (Johns Hopkins University), Albert Cardona (Howard Hughes Medical Institute, MRC Laboratory of Molecular Biology), Marta Zlatic (Howard Hughes Medical Institute, MRC Laboratory of Molecular Biology)
Résumé : Abstract Brains contain networks of interconnected neurons, so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an insect brain ( Drosophila larva) with rich behavior, including learning, value-computation, and action-selection, comprising 3,013 neurons and 544,000 synapses. We characterized neuron-types, hubs, feedforward and feedback pathways, and cross-hemisphere and brain-nerve cord interactions. We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brain’s most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled powerful machine learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits. One-Sentence Summary We generated a synaptic-resolution brain connectome and characterized its connection types, neuron types, and circuit motifs.