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- W2943001153 endingPage "187" @default.
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- W2943001153 abstract "The neurosciences have developed methods that outpace most other biomedical fields in terms of acquired bytes. We review how the information content and analysis challenge of such data indicates that electron microscopy (EM)-based connectomics is an especially hard problem. Here, as in many other current machine learning applications, the need for excessive amounts of labelled data while utilizing only a small fraction of available raw image data for algorithm training illustrates the still fundamental gap between artificial and biological intelligence. Substantial improvements of label and energy efficiency in machine learning may be required to address the formidable challenge of acquiring the nanoscale connectome of a human brain." @default.
- W2943001153 created "2019-05-09" @default.
- W2943001153 creator A5027966876 @default.
- W2943001153 creator A5052833920 @default.
- W2943001153 creator A5055319639 @default.
- W2943001153 creator A5069874653 @default.
- W2943001153 date "2019-04-01" @default.
- W2943001153 modified "2023-10-16" @default.
- W2943001153 title "Big data in nanoscale connectomics, and the greed for training labels" @default.
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- W2943001153 doi "https://doi.org/10.1016/j.conb.2019.03.012" @default.
- W2943001153 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31055238" @default.
- W2943001153 hasPublicationYear "2019" @default.
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