The connectome of an insect brain

Author:

Winding Michael123ORCID,Pedigo Benjamin D.4ORCID,Barnes Christopher L.25ORCID,Patsolic Heather G.67ORCID,Park Youngser8,Kazimiers Tom39ORCID,Fushiki Akira310ORCID,Andrade Ingrid V.11,Khandelwal Avinash3ORCID,Valdes-Aleman Javier13ORCID,Li Feng3,Randel Nadine12ORCID,Barsotti Elizabeth25ORCID,Correia Ana25ORCID,Fetter Richard D.312ORCID,Hartenstein Volker11ORCID,Priebe Carey E.68,Vogelstein Joshua T.48ORCID,Cardona Albert235ORCID,Zlatic Marta123ORCID

Affiliation:

1. University of Cambridge, Department of Zoology, Cambridge, UK.

2. MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK.

3. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.

4. Johns Hopkins University, Department of Biomedical Engineering, Baltimore, MD, USA.

5. University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK.

6. Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, MD, USA.

7. Accenture, Arlington, VA, USA.

8. Johns Hopkins University, Center for Imaging Science, Baltimore, MD, USA.

9. kazmos GmbH, Dresden, Germany.

10. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.

11. University of California Los Angeles, Department of Molecular, Cell and Developmental Biology, Los Angeles, CA, USA.

12. Stanford University, Stanford, CA, USA.

Abstract

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.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Cited by 79 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3