High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination
Abstract
© 2016 Opportunities offered by new neuro-technologies are threatened by lack of coherent plans to analyze, manage, and understand the data. High-performance computing will allow exploratory analysis of massive datasets stored in standardized formats, hosted in open repositories, and integrated with simulations.
- Authors:
-
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Biolgoical Systems and Engineering Division; Univ. of California, San Francisco, CA (United States). Kavli Inst. for Fundamental Neuroscience; Univ. of California, Berkeley, CA (United States). Helen Wills Neuroscience Inst.
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Center for Computing Research
- The Kavli Foundation, Oxnard, CA (United States)
- Google Research, Mountain View, CA (United States)
- Julich Research Centre (Germany). Inst. of Neuroscience and Medicine (INM-6) and INst. for Advanced Simulation (IAS-6)
- Julich Research Centre (Germany). Inst. of Neuroscience and Medicine (INM-6) and INst. for Advanced Simulation (IAS-6); RWTH Aachen Univ. (Germany). Dept. of Psychiatry and Dept. of Physics
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Computational Research Division
- Univ. of California, San Francisco, CA (United States). Kavli Inst. for Fundamental Neuroscience, Howard Hughes Medical Inst. and Dept. of Physiology
- Argonne National Lab. (ANL), Argonne, IL (United States). Nanoscience Division; Univ. of Chicago, IL (United States). Dept. of Neurobiology
- Allen Inst. for Brain Science, Seattle, WA (United States)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Univ. of California, Berkeley, CA (United States). Redwood Center for Theoretical Neuroscience
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). NERSC
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1332918
- Alternate Identifier(s):
- OSTI ID: 1461976
- Report Number(s):
- SAND2016-11039J
Journal ID: ISSN 0896-6273; PII: S0896627316307851; TRN: US1700177
- Grant/Contract Number:
- AC04-94AL85000; AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Neuron
- Additional Journal Information:
- Journal Volume: 92; Journal Issue: 3; Journal ID: ISSN 0896-6273
- Publisher:
- Cell Press - Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING
Citation Formats
Bouchard, Kristofer E., Aimone, James B., Chun, Miyoung, Dean, Thomas, Denker, Michael, Diesmann, Markus, Donofrio, David D., Frank, Loren M., Kasthuri, Narayanan, Koch, Chirstof, Ruebel, Oliver, Simon, Horst D., Sommer, Friedrich T., and ., Prabhat. High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination. United States: N. p., 2016.
Web. doi:10.1016/j.neuron.2016.10.035.
Bouchard, Kristofer E., Aimone, James B., Chun, Miyoung, Dean, Thomas, Denker, Michael, Diesmann, Markus, Donofrio, David D., Frank, Loren M., Kasthuri, Narayanan, Koch, Chirstof, Ruebel, Oliver, Simon, Horst D., Sommer, Friedrich T., & ., Prabhat. High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination. United States. https://doi.org/10.1016/j.neuron.2016.10.035
Bouchard, Kristofer E., Aimone, James B., Chun, Miyoung, Dean, Thomas, Denker, Michael, Diesmann, Markus, Donofrio, David D., Frank, Loren M., Kasthuri, Narayanan, Koch, Chirstof, Ruebel, Oliver, Simon, Horst D., Sommer, Friedrich T., and ., Prabhat. Wed .
"High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination". United States. https://doi.org/10.1016/j.neuron.2016.10.035. https://www.osti.gov/servlets/purl/1332918.
@article{osti_1332918,
title = {High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination},
author = {Bouchard, Kristofer E. and Aimone, James B. and Chun, Miyoung and Dean, Thomas and Denker, Michael and Diesmann, Markus and Donofrio, David D. and Frank, Loren M. and Kasthuri, Narayanan and Koch, Chirstof and Ruebel, Oliver and Simon, Horst D. and Sommer, Friedrich T. and ., Prabhat},
abstractNote = {© 2016 Opportunities offered by new neuro-technologies are threatened by lack of coherent plans to analyze, manage, and understand the data. High-performance computing will allow exploratory analysis of massive datasets stored in standardized formats, hosted in open repositories, and integrated with simulations.},
doi = {10.1016/j.neuron.2016.10.035},
journal = {Neuron},
number = 3,
volume = 92,
place = {United States},
year = {Wed Nov 02 00:00:00 EDT 2016},
month = {Wed Nov 02 00:00:00 EDT 2016}
}
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Works referenced in this record:
The quiet revolution of numerical weather prediction
journal, September 2015
- Bauer, Peter; Thorpe, Alan; Brunet, Gilbert
- Nature, Vol. 525, Issue 7567
Beta oscillations in a large-scale sensorimotor cortical network: Directional influences revealed by Granger causality
journal, June 2004
- Brovelli, A.; Ding, M.; Ledberg, A.
- Proceedings of the National Academy of Sciences, Vol. 101, Issue 26
Dimensionality reduction for large-scale neural recordings
journal, August 2014
- Cunningham, John P.; Yu, Byron M.
- Nature Neuroscience, Vol. 17, Issue 11
On simplicity and complexity in the brave new world of large-scale neuroscience
journal, June 2015
- Gao, Peiran; Ganguli, Surya
- Current Opinion in Neurobiology, Vol. 32
Neo: an object model for handling electrophysiology data in multiple formats
journal, January 2014
- Garcia, Samuel; Guarino, Domenico; Jaillet, Florent
- Frontiers in Neuroinformatics, Vol. 8
Worldwide initiatives to advance brain research
journal, August 2016
- Grillner, Sten; Ip, Nancy; Koch, Christof
- Nature Neuroscience, Vol. 19, Issue 9
Inferring cortical function in the mouse visual system through large-scale systems neuroscience
journal, July 2016
- Hawrylycz, Michael; Anastassiou, Costas; Arkhipov, Anton
- Proceedings of the National Academy of Sciences, Vol. 113, Issue 27
Spiking network simulation code for petascale computers
journal, October 2014
- Kunkel, Susanne; Schmidt, Maximilian; Eppler, Jochen M.
- Frontiers in Neuroinformatics, Vol. 8
The big data challenges of connectomics
journal, October 2014
- Lichtman, Jeff W.; Pfister, Hanspeter; Shavit, Nir
- Nature Neuroscience, Vol. 17, Issue 11
Reconstruction and Simulation of Neocortical Microcircuitry
journal, October 2015
- Markram, Henry; Muller, Eilif; Ramaswamy, Srikanth
- Cell, Vol. 163, Issue 2
Network Motifs: Simple Building Blocks of Complex Networks
journal, October 2002
- Milo, R.
- Science, Vol. 298, Issue 5594
Works referencing / citing this record:
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- Dai, Kael; Hernando, Juan; Billeh, Yazan N.
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- Islam, Md Nurul; Martin, Seán K.; Aggleton, John P.
- Wellcome Open Research, Vol. 4
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- Neurocomputing, Vol. 428
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- Dai, Kael; Hernando, Juan; Billeh, Yazan N.
- PLOS Computational Biology, Vol. 16, Issue 2
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posted_content, September 2019
- Dai, Kael; Hernando, Juan; Billeh, Yazan N.
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journal, January 2019
- Islam, Md Nurul; Martin, Seán K.; Aggleton, John P.
- Wellcome Open Research, Vol. 4
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journal, February 2017
- Manninen, Tiina; Havela, Riikka; Linne, Marja-Leena
- Frontiers in Neuroinformatics, Vol. 11
Arkheia: Data Management and Communication for Open Computational Neuroscience
journal, March 2018
- Antolík, Ján; Davison, Andrew P.
- Frontiers in Neuroinformatics, Vol. 12
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journal, May 2018
- Manninen, Tiina; Aćimović, Jugoslava; Havela, Riikka
- Frontiers in Neuroinformatics, Vol. 12