Difference between revisions of "Journal:Arkheia: Data management and communication for open computational neuroscience"
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'''Keywords''': computational modeling, workflow, publish, neuroscience, tool | '''Keywords''': computational modeling, workflow, publish, neuroscience, tool | ||
==Introduction== | |||
For most of its history, computational neuroscience has focused on relatively homogeneous models, targeting one or at most a handful of features of neural processing at a time. Such a classical reductionist approach is starting to be supplemented by more integrative strategies that utilize increasingly complex and heterogeneous neural network models in order to explain within a single model instance an increasingly broad range of neural phenomena.<ref name="MarkramTheBlue06">{{cite journal |title=The blue brain project |journal=Nature Reviews Neuroscience |author=Markram, H. |volume=7 |issue=2 |pages=153–60 |year=2006 |doi=10.1038/nrn1848 |pmid=16429124}}</ref><ref name="RanganMulti09">{{cite journal |title=Multiscale modeling of the primary visual cortex |journal=IEEE Engineering in Medicine and Biology Magazine |author=Rangan, A.V.; Tao, L.; Kovacic, G.; Cai, D. |volume=28 |issue=3 |pages=19–24 |year=2009 |doi=10.1109/MEMB.2009.932803 |pmid=19457730}}</ref><ref name="MarkramIntro11">{{cite journal |title=Introducing the Human Brain Project |journal=Procedia Computer Science |author=Markram, H.; Meir, K.; Lippert, T. et al. |volume=7 |pages=39–42 |year=2011 |doi=10.1016/j.procs.2011.12.015}}</ref><ref name="KochNeuro12">{{cite journal |title=Neuroscience: Observatories of the mind |journal=Nature |author=Koch, C.; Reid, R.C. |volume=483 |issue=7390 |pages=397–8 |year=2011 |doi=10.1038/483397a |pmid=22437592}}</ref><ref name="BouchardHigh16">{{cite journal |title=High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination |journal=Neuron |author=Bouchard, K.E.; Aimone, J.B.; Chun, M. et al. |volume=92 |issue=3 |pages=628-631 |year=2016 |doi=10.1016/j.neuron.2016.10.035 |pmid=27810006}}</ref><ref name="HawrylyczInfer16">{{cite journal |title=Inferring cortical function in the mouse visual system through large-scale systems neuroscience |journal=Proceedings of the National Academy of Sciences of the Unites States of America |author=Hawrylycz, M.; Anastassiou, C.; Arkhipov, A. et al. |volume=113 |issue=27 |pages=7337–44 |year=2016 |doi=10.1073/pnas.1512901113 |pmid=27382147 |pmc=PMC4941493}}</ref> Even though the classical reductionist approach will remain important, an integrative research program seems unavoidable if we are to understand a complex dynamical system such as the cortex (or the entire brain), whose computational power is underlined by the dynamical interplay of all its anatomical and functional constituents, rather than just their simple aggregation. Given its sheer scope and complexity, such an integrative research program is unlikely to succeed if implemented by individual scientists or even individual teams. Rather, a systematic incremental strategy relying on cooperation within the entire field will be required, whereupon new models build directly on previous work, and all models are extensively validated against biological data and compared against previous models based on an increasingly exhaustive set of measures. These trends herald the shift of focus from model creation and simulation to model analysis and testing. | |||
==References== | ==References== | ||
Revision as of 20:23, 2 April 2018
| Full article title | Arkheia: Data management and communication for open computational neuroscience |
|---|---|
| Journal | Frontiers in Neuroinformatics |
| Author(s) | Antolik, Ján; Davison, Andrew P. |
| Author affiliation(s) | Institut de la Vision, Centre National de la Recherche Scientifique |
| Primary contact | Email: antolikjan at gmail dot com |
| Editors | Valdes-Sosa, Pedro Antonio |
| Year published | 2018 |
| Volume and issue | 12 |
| Page(s) | 6 |
| DOI | 10.3389/fninf.2018.00006 |
| ISSN | 1662-5196 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://www.frontiersin.org/articles/10.3389/fninf.2018.00006/full |
| Download | https://www.frontiersin.org/articles/10.3389/fninf.2018.00006/pdf (PDF) |
 |
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Abstract
Two trends have been unfolding in computational neuroscience during the last decade. First, focus has shifted to increasingly complex and heterogeneous neural network models, with a concomitant increase in the level of collaboration within the field (whether direct or in the form of building on top of existing tools and results). Second, general trends in science have shifted toward more open communication, both internally, with other potential scientific collaborators, and externally, with the wider public. This multi-faceted development toward more integrative approaches and more intense communication within and outside of the field poses major new challenges for modelers, as currently there is a severe lack of tools to help with automatic communication and sharing of all aspects of a simulation workflow to the rest of the community. To address this important gap in the current computational modeling software infrastructure, here we introduce Arkheia, a web-based open science platform for computational models in systems neuroscience. It provides an automatic, interactive, graphical presentation of simulation results, experimental protocols, and interactive exploration of parameter searches in a browser-based application. Arkheia is focused on the automatic presentation of these resources with minimal manual input from users. Arkheia is written in a modular fashion, with a focus on future development of the platform. The platform is designed in an open manner, with a clearly defined and separated application programming interface (API) for database access, so that any project can write its own back-end, translating its data into the Arkheia database format. Arkheia is not a centralized platform, but it allows any user (or group of users) to set up their own repository, either for public access by the general population, or locally for internal use. Overall, Arkheia provides users with an automatic means to communicate information about not only their models but also individual simulation results and the entire experimental context in an approachable, graphical manner, thus facilitating the user's ability to collaborate in the field and outreach to a wider audience.
Keywords: computational modeling, workflow, publish, neuroscience, tool
Introduction
For most of its history, computational neuroscience has focused on relatively homogeneous models, targeting one or at most a handful of features of neural processing at a time. Such a classical reductionist approach is starting to be supplemented by more integrative strategies that utilize increasingly complex and heterogeneous neural network models in order to explain within a single model instance an increasingly broad range of neural phenomena.[1][2][3][4][5][6] Even though the classical reductionist approach will remain important, an integrative research program seems unavoidable if we are to understand a complex dynamical system such as the cortex (or the entire brain), whose computational power is underlined by the dynamical interplay of all its anatomical and functional constituents, rather than just their simple aggregation. Given its sheer scope and complexity, such an integrative research program is unlikely to succeed if implemented by individual scientists or even individual teams. Rather, a systematic incremental strategy relying on cooperation within the entire field will be required, whereupon new models build directly on previous work, and all models are extensively validated against biological data and compared against previous models based on an increasingly exhaustive set of measures. These trends herald the shift of focus from model creation and simulation to model analysis and testing.
References
- ↑ Markram, H. (2006). "The blue brain project". Nature Reviews Neuroscience 7 (2): 153–60. doi:10.1038/nrn1848. PMID 16429124.
- ↑ Rangan, A.V.; Tao, L.; Kovacic, G.; Cai, D. (2009). "Multiscale modeling of the primary visual cortex". IEEE Engineering in Medicine and Biology Magazine 28 (3): 19–24. doi:10.1109/MEMB.2009.932803. PMID 19457730.
- ↑ Markram, H.; Meir, K.; Lippert, T. et al. (2011). "Introducing the Human Brain Project". Procedia Computer Science 7: 39–42. doi:10.1016/j.procs.2011.12.015.
- ↑ Koch, C.; Reid, R.C. (2011). "Neuroscience: Observatories of the mind". Nature 483 (7390): 397–8. doi:10.1038/483397a. PMID 22437592.
- ↑ Bouchard, K.E.; Aimone, J.B.; Chun, M. et al. (2016). "High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination". Neuron 92 (3): 628-631. doi:10.1016/j.neuron.2016.10.035. PMID 27810006.
- ↑ Hawrylycz, M.; Anastassiou, C.; Arkhipov, A. et al. (2016). "Inferring cortical function in the mouse visual system through large-scale systems neuroscience". Proceedings of the National Academy of Sciences of the Unites States of America 113 (27): 7337–44. doi:10.1073/pnas.1512901113. PMC PMC4941493. PMID 27382147. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4941493.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. The original article lists references alphabetically, but this version — by design — lists them in order of appearance. What were originally footnotes have been turned into inline external links.
