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COMPUTATIONAL NEUROSCIENCE

Efficiently pruning brain connectomes

Nature Computational Science volume 2, pages 288–289 (2022)Cite this article

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Characterizing the brain’s connectome at multiple scales is essential for unraveling fundamental principles of cortical information processing and how it impacts behavior. A GPU-based implementation for connectome pruning is proposed, achieving greater than 100-fold speedups over previous CPU-based implementations.

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Fig. 1: ReAl-LiFE prunes and evaluates human connectomes on GPUs.

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Authors and Affiliations

  1. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China

    Xi-Nian Zuo

  2. Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

    Xi-Nian Zuo

  3. National Basic Science Data Center, Beijing, China

    Xi-Nian Zuo

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  1. Xi-Nian Zuo
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Correspondence to Xi-Nian Zuo.

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Zuo, XN. Efficiently pruning brain connectomes. Nat Comput Sci 2, 288–289 (2022). https://doi.org/10.1038/s43588-022-00252-x

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