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  Large-Scale Automated Histology in the Pursuit of Connectomes

Kleinfeld, D., Bharioke, A., Blinder, P., Bock, D., Briggman, K., Chklovskii, D., et al. (2011). Large-Scale Automated Histology in the Pursuit of Connectomes. Journal of Molecular Neuroscience, 31(45), 16125-16138. doi:10.1523/JNEUROSCI.4077-11.2011.

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 Creators:
Kleinfeld, D, Author
Bharioke, A, Author
Blinder, P, Author
Bock, DD, Author
Briggman, KL, Author
Chklovskii, DD, Author
Denk, W, Author
Helmstaedter, M, Author
Kaufhold, JP, Author
Lee, WC-A, Author
Meyer, HS, Author
Micheva , KD, Author
Oberlaender, M1, Author           
Prohaska, S, Author
Reid, RC, Author
Smith, SJ, Author
Takemura, S, Author
Tsai, PS, Author
Sakmann, B, Author
Affiliations:
1Digital Neuroanatomy, Max Planck Florida Institute, Jupiter, Florida , ou_persistent22              

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 Abstract: How does the brain compute? Answering this question necessitates neuronal connectomes, annotated graphs of all synaptic connections within defined brain areas. Further, understanding the energetics of the brain's computations requires vascular graphs. The assembly of a connectome requires sensitive hardware tools to measure neuronal and neurovascular features in all three dimensions, as well as software and machine learning for data analysis and visualization. We present the state of the art on the reconstruction of circuits and vasculature that link brain anatomy and function. Analysis at the scale of tens of nanometers yields connections between identified neurons, while analysis at the micrometer scale yields probabilistic rules of connection between neurons and exact vascular connectivity.

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 Dates: 2011-11
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1523/JNEUROSCI.4077-11.2011
BibTex Citekey: KleinfeldBBBBCDHKLMMOPRSTTS2011
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Title: Journal of Molecular Neuroscience
  Other : Journal of Molecular Neuroscience
Source Genre: Journal
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Publ. Info: Cambridge, MA : Birkhäuser Boston
Pages: - Volume / Issue: 31 (45) Sequence Number: - Start / End Page: 16125 - 16138 Identifier: ISSN: 0895-8696
CoNE: https://pure.mpg.de/cone/journals/resource/954925560555