The connectome of an insect brain

• 成果类型: Article
• 署名作者: Winding, Michael; Pedigo, Benjamin D.; Barnes, Christopher L.; Patsolic, Heather G.; Park, Youngser; Kazimiers, Tom; Fushiki, Akira; Andrade, Ingrid V.; Khandelwal, Avinash; Valdes-Aleman, Javier; Li, Feng; Randel, Nadine; Barsotti, Elizabeth; Correia, Ana; Fetter, Richard D.; Hartenstein, Volker; Priebe, Carey E.; Vogelstein, Joshua T.; Cardona, Albert; Zlatic, Marta
• 署名单位: University of Cambridge; MRC Laboratory Molecular Biology; Howard Hughes Medical Institute; Johns Hopkins University; University of Cambridge; Johns Hopkins University; Johns Hopkins University; Columbia University; University of California System; University of California Los Angeles; Stanford University
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-9074
• DOI: 10.1126/science.add9330
• 发表日期: 2023-03-10
• 页码: 995-+
• 关键词: neural activity; nervous-system; rich-club; drosophila; circuit; neurons; inhibition; anatomy; network; interneurons

摘要

Brains contain networks of interconnected neurons and so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an entire insect brain (Drosophila larva) with rich behavior, including learning, value computation, and action selection, comprising 3016 neurons and 548,000 synapses. We characterized neuron types, hubs, feedforward and feedback pathways, as well as 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 state-of-the-art deep learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits.