Journal
CURRENT OPINION IN NEUROBIOLOGY
Volume 55, Issue -, Pages 180-187Publisher
CURRENT BIOLOGY LTD
DOI: 10.1016/j.conb.2019.03.012
Keywords
-
Categories
Funding
- 16 N.I.H. Institutes and Centers [1U54MH091657]
- McDonnell Center for Systems Neuroscience at Washington University
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available