Journal:Arkheia: Data management and communication for open computational neuroscience

From LIMSWiki
Revision as of 21:17, 2 April 2018 by Shawndouglas (talk | contribs) (Saving and adding more.)
Jump to navigationJump to search
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)
This article should not be considered complete until this message box has been removed. This is a work in progress.

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.

At the same time, this increasing need for collaboration within computational neuroscience is accompanied by a more general trend in science toward more open communication, both internally, with other potential scientific collaborators, and externally, with the wider public. Many examples have by now shown the value of such open science approaches[7][8] to promote one's research and find new collaborations. Engagement of a non-academic enthusiast audience via open-science platforms can not only improve the public outreach of one's research program, but also contribute to the core scientific development. However, the effectiveness of such an opening up of one's research is critically dependent on the ease with which outsiders can engage with the exposed resources, which in turn critically depends on the quality of the (software) infrastructure used to serve said resources.

This multi-faceted development toward more integrative approaches and intensifying communication within and outside the field poses major new challenges for the software infrastructure available to computational neuroscientists. The set of tools involved in a typical modeler's workflow is expanding concurrently with growing complexity in the metadata flowing between them. Meanwhile the requirements for their efficient interfacing with the outside world (whether in the form of human users or other software tools) is growing. This growing complexity of the tasks involved in the typical modeler's workflow is putting strain on researchers, who are required to manage increasingly more complex software infrastructure while spending a substantial portion of their work time either writing ad-hoc software solutions to cover poorly supported aspects of the workflow or handling them manually. This situation is clearly less than ideal, slowing down the pace of research while introducing errors and hindering its reproducibility.

The last four decades have seen numerous additions to the ecosystem of computational neuroscience tools, including efficient, well tested, and highly usable simulators such as Neuron[9], NEST[10], Brian[11], NENGO[12], and others; data management and parameter exploration tools such as PyNN[13], Neo[14], Lancet[15], Pypet[16], and others[17]; neural data analysis toolkits SpikeViewer (Pröpper and Obermayer, 2013) HRLAnalysis (Thibeault et al., 2014), NeuroTools (http://neuralensemble.org/NeuroTools), Elephant (http://neuralensemble.org/elephant), and integrated workflow and simulation environments such as VirtualBrain, psychopy_ext, or Mozaik (Antolík and Davison, 2013; Kubilius, 2014; Woodman et al., 2014). Despite this rapid progress, the interfacing between the tools and communication with third parties (whether users or tools) remains limited, hindering the future development of integrative collaborative approaches in computational systems neuroscience. We identify the following aspects of the modeling workflow, all with implications for communication and interfacing, that are currently poorly supported and are key to resolving the outlined limitations of the present infrastructure:

References

  1. ↑ Markram, H. (2006). "The blue brain project". Nature Reviews Neuroscience 7 (2): 153–60. doi:10.1038/nrn1848. PMID 16429124. 
  2. ↑ 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. 
  3. ↑ 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. 
  4. ↑ Koch, C.; Reid, R.C. (2011). "Neuroscience: Observatories of the mind". Nature 483 (7390): 397–8. doi:10.1038/483397a. PMID 22437592. 
  5. ↑ 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. 
  6. ↑ 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. 
  7. ↑ Anderson, D.P.; Cobb, J.; Korpela, E. et al. (2002). "SETI@home: An experiment in public-resource computing". Communications of the ACM 45 (11): 56–61. doi:10.1145/581571.581573. 
  8. ↑ Szigeti, B.; Gleeson, P.; Vella, M. et al. (2014). "OpenWorm: An open-science approach to modeling Caenorhabditis elegans". Frontiers in Computational Neueroscience 8: 137. doi:10.3389/fncom.2014.00137. PMC PMC4217485. PMID 25404913. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4217485. 
  9. ↑ Carnevale, N.T.; Hines, M.L. (2006). The Neuron Book. Cambridge University Press. pp. 480. ISBN 9780521843218. 
  10. ↑ Gewaltig, M.-O.; Diesmann, M.. "NEST (NEural Simulation Tool)". Scholarpedia 2 (4): 1430. doi:10.4249/scholarpedia.1430. 
  11. ↑ Stimberg, M.; Goodman, D.F.; Benichoux, V.; Brette, R.. "Equation-oriented specification of neural models for simulations". Frontiers in Neuroinformatics 8: 6. doi:10.3389/fninf.2014.00006. PMC PMC3912318. PMID 24550820. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3912318. 
  12. ↑ Bekolay, T.; Bergstra, J.; Hunsberger, E. et al.. "Nengo: a Python tool for building large-scale functional brain models". Frontiers in Neuroinformatics 7: 48. doi:10.3389/fninf.2013.00048. PMC PMC3880998. PMID 24431999. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3880998. 
  13. ↑ Davison, A.P.; Brüderle, D.; Eppler, J. et al.. "PyNN: A Common Interface for Neuronal Network Simulators". Frontiers in Neuroinformatics 2: 11. doi:10.3389/neuro.11.011.2008. PMC PMC2634533. PMID 19194529. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2634533. 
  14. ↑ Garcia, S.; Guarino, D.; Jaillet, F. et al.. "Neo: An object model for handling electrophysiology data in multiple formats". Frontiers in Neuroinformatics 8: 10. doi:10.3389/fninf.2014.00010. PMC PMC3930095. PMID 24600386. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3930095. 
  15. ↑ Stevens, J.L.; Elver, M.; Bednar, J.A.. "An automated and reproducible workflow for running and analyzing neural simulations using Lancet and IPython Notebook". Frontiers in Neuroinformatics 7: 44. doi:10.3389/fninf.2013.00044. PMC PMC3874632. PMID 24416014. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3874632. 
  16. ↑ Meyer, R.; Obermayer, K.. "pypet: A Python Toolkit for Data Management of Parameter Explorations". Frontiers in Neuroinformatics 10: 38. doi:10.3389/fninf.2016.00038. PMC PMC4996826. PMID 27610080. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996826. 
  17. ↑ Friedrich, P.; Vella, M.; Gulyás, A.I. et al.. "A flexible, interactive software tool for fitting the parameters of neuronal models". Frontiers in Neuroinformatics 8: 63. doi:10.3389/fninf.2014.00063. PMC PMC4091312. PMID 25071540. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4091312. 

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.

Retrieved from "https://www.limswiki.org/index.php?title=Journal:Arkheia:_Data_management_and_communication_for_open_computational_neuroscience&oldid=32763"