A federated design for a neurobiological simulation engine: The CBI federated software architecture

PLoS ONE

Volumn 7, Issue 1, 2012, Pages

  Cornelis, Hugo ^a   Coop, Allan D ^a   Bower, James M ^a  

^a UNIVERSITY OF TEXAS HEALTH SCIENCE CENTER AT SAN ANTONIO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; COMPUTER INTERFACE; COMPUTER PROGRAM; DATA BASE; EQUIPMENT DESIGN; INFORMATION PROCESSING; MARKUP LANGUAGE; MATHEMATICAL MODEL; NEUROBIOLOGY; SIMULATION; SIMULATOR; BIOLOGICAL MODEL; BIOLOGY; COMPUTER SIMULATION; METHODOLOGY; WORKFLOW;

COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DATABASES AS TOPIC; MODELS, BIOLOGICAL; NEUROBIOLOGY; SOFTWARE; USER-COMPUTER INTERFACE; WORKFLOW;

EID: 84855393704     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0028956     Document Type: Article

References (62)

1. Lapicque L, (1907) Recherches quantitative sur l'excitabilitie électrique des nerfs traitée comme une polarisation. General Pathology and Pathological Physiology 9: 620-625.
2. Hodgkin A, Huxley A, (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology (Lond) 117: 500-544.
3. Rall W, (1957) Membrane time constants of motoneurons. Science 126: 454.
4. Marr DA, (1982) Vision: A Computational Investigation into the Human Representation and Processing of Visual Information Cambridge, MA MIT Press.
5. Rall W, (1959) Branching dendritic trees and motoneurons membrane resistivity. Experimental Neurology 1: 491-527.
6. Schwartz E, (1990) Computational Neuroscience Cambridge, MA MIT Press.
7. Moore JW, (2010) A personal view of the early development of computational neuroscience in the usa. Frontiers in Computational Neuroscience 4: 20.
8. Hines M, (1993) NEURON-A program for simulation of nerve equations. In: Eeckman F, editors. Neural Systems: Analysis and Modeling Norwell, MA Kluwer pp. 127-136.
9. Hines M, (1984) Efficient computation of branched nerve equations. International Journal of Bio- Medical Computing 15: 69-76.
10. Hines M, (1989) A program for simulation of nerve equations with branching geometries. International Journal of Bio-Medical Computing 24: 55-68.
11. Wilson M, Bhalla U, Uhley J, Bower J, (1989) GENESIS: A system for simulating neural networks. In: Anderson D, editors. Advances in Neural Information Processing Systems, American Institute of Physics: New York pp. 485-492.
12. Wilson M, Bower J, (1988) A computer simulation of olfactory cortex with functional implications for storage and retrieval of olfactory information. In: Anderson D, editors. Advances in Neural Information Processing Systems, American Institute of Physics: New York pp. 114-126.
13. Wilson M, Bower J, (1989) Computer simulation of oscillatory behavior in cerebral cortical networks. In: Anderson D, editors. Advances in Neural Information Processing Systems, American Institute of Physics: New York pp. 84-91.
14. Migliore M, Cannia C, Lytton W, Markram H, Hines M, (2006) Parallel network simulations with neuron. Journal of Computational Neuroscience 21: 119-129.
15. Hereld M, Stevens R, van Drongelen W, Lee H, (2004) Developing a petascale neural simulation. pp. 3999-4002 In: 26th Annual International Conference of IEEE Engineering in Medicine and Biology Society (EMBS). volume 2.
16. Diesmann M, Gewaltig M, (2002) NEST: An environment for neural systems simulations. In: Plesser T, Volker M, editors. Forschung und wisschenschaftliches Rechnen, Beitr̈age zum Heinz- Billing-Preis 2001, Göttingen: Gesellschaft f̈ur wissenschaftliche Datenverarbeitung, volume 58 of GWDG-Bericht pp. 43-70.
17. Touretzky D, Ladsariya A, Albert M, Johnson J, Daw N, (2003) HHsim: An open source, real-time, graphical Hodgkin-Huxley simulator. Society for Neuroscience Abstracts 29: 24.13.
18. Hoops S, Sahle S, Gauges R, Lee C, Pahle J, et al. (2006) COPASI-A COmplex PAthway SImulator. Bioinformatics 22: 3067-3074.
19. Hucka M, Finney A, Bornstein B, Keating S, Shapiro B, et al. (2004) Evolving a lingua franca and associated software infrastructure for computational systems biology: The Systems Biology Markup Language (SBML) project. Systems Biology 1: 41-53.
20. Stiles J, Bartol T, (2001) Monte Carlo methods for simulating realistic synaptic microphysiology using MCell. In: De Schutter E, editors. Computational Neuroscience: Realistic Modeling for Experimentalists, CRC Press: Boca Raton pp. 87-127.
21. Cannon R, Gewaltig M, Gleeson P, Bhalla U, Cornelis H, et al. (2007) Interoperability of neuroscience modeling software: current status and future directions. Neuroinformatics 5: 127-138.
22. Stodden V, (2009) The legal framework for reproducible scientific research: Licensing and copyright. Computing in Science and Engineering 11: 35-40.
23. Gleeson P, Crook S, Cannon R, Hines M, Billings G, et al. (2010) NeuroML: A language for describing data driven models of neurons and networks with a high degree of biological detail. PLoS Computational Biology 6: e1000815.
24. Subhasis R, Bhalla U, (2008) PyMOOSE: Interoperable scripting in Python for MOOSE. Frontiers in Neuroinformatics 2: 6.
25. Djurfeldt M, Hjorth J, Eppler J, Dudani N, Hellas M, et al. (2010) Run-time interoperability between neuronal network simulators based on the MUSIC framework. Neuroinformatics 8: 43-60.
26. Davison A, Brüderle D, Eppler J, Kremkow J, Muller E, et al. (2008) PyNN: A common interface for neuronal network simulators. Frontiers in Neuroinformatics 2: 11.
27. Cannon RC, Hasselmo ME, Koene RA, (2003) From biophysics to behavior: Catacomb2 and the design of biologically plausible models for spatial navigation. Neuroinformatics 1: 3-42.
28. Cornelis H, Rodriguez A, Coop A, Bower J, (2012) Python as a federation tool for GENESIS 3.0. PLoS ONE 7 (1): 29018.
29. Apostel L, (1961) Towards the formal study of models in the non-formalsciences. In: Freudenthal H, editors. The Concepts and the Role of the Model in Mathematics and Natural and Social Sciences Dordrecht: The Netherlands D. Reidel Publishing Company pp. 1-37.
30. Belhajjame K, Collet C, Vargas-Solar G, (2001) A flexible workflow model for process-oriented applications. 2nd International Conference on Web Information Systems Engineering IEEE, volume 1 pp. 72-80.
31. Cornelis H, De Schutter E, (2002) Tutorial: Simulations with Genesis using Hsolve. Course material. Available from http://genesis-sim.org/filemanager/active?fid=95, accessed 2011 Dec 11.
32. Dijkstra E, (1982) On the role of scientific thought. In: Dijkstra E, editors. Selected Writings on Computing: A Personal Perspective New York, NY Springer-Verlag pp. 60-66.
33. De Schutter E, Bower J, (1994) Simulated responses of cerebellar Purkinje cells are independent of the dentritic location of granule cell synaptic inputs. Proceedings of the National Academy of Sciences USA 91: 4736-4740.
34. Robbins KA, Robinson M, Senseman D, (2004) Visualizing cortical waves and timing from data. IEEE Visualization pp. 401-408.
35. Gleeson P, Steuber V, Silver R, (2007) neuroConstruct: A tool for modeling networks of neurons in 3D space. Neuron 54: 219-235.
36. Nordlie E, Plesser HE, (2009) Visualizing neuronal network connectivity with connectivity pattern tables. Frontiers in Neuroinformatics 3: 39.
37. De Schutter E, Bower J, (1994) An active membrane model of the cerebellar purkinje cell I. Simulation of current clamp in slice. Journal of Nerurophysiology 71: 375-400.
38. De Schutter E, Bower JM, (1994) An active membrane model of the cerebellar purkinje cell II. simulation of synaptic responses. Journal of Nerurophysiology 71: 401-419.
39. Bower JM, Beeman D, eds. (1998) The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System. Springer-Verlag, New York, second edition.
40. Abramowitz M, Stegun I, (1965) Handbook of Mathematical Functions. Dover Publications, New York.
41. Crank J, Nicholson P, (1947) A practical method for numerical evaluation of solutions of partial differential equations of the heat conduction type. Proceedings of the Cambridge Philosophical Society 43: 50-67.
42. Borg-Graham LJ, (2000) Additional efficient computation of branched nerve equations: Adaptive time step and ideal voltage clamp. Journal of Computational Neuroscience 8: 209-226.
43. Hines M, Markram H, Schuermann F, (2008) Fully implicit parallel simulation of single neurons. Journal of Computational Neuroscience 25: 439-448.
44. Mernik M, Heering J, Sloane AM, (2005) When and how to develop domain-specific languages. ACM Comput Surv 37: 316-344.
45. Law E, Roto V, Hassenzahl M, Vermeeren A, Kort J, (2009) Understanding, scoping and defining user experience: A survey approach. Proceedings of Human Factors in Computing Systems conference Boston, MA, USA pp. 719-728.
46. Eckerson W, (1995) Three tier client/server architecture: Achieving scalability, performance, and efficiency in client server applications. Open Information Systems 10, 1 3: 1-20.
47. (2008) Service-oriented architecture. World Wide Web. http://en.wikipedia.org/wiki/Serviceoriented architecture, accessed 2011 Dec 11.
48. Humby E, eds. (1973) Programs from Decision Tables New York American Elsevier.
49. Köhn D, Le Novère N, (2008) SED-ML-An XML format for the implementation of the MIASE guidlines. In: Heiner M, Uhrmacher A, editors. Proceedings of the 6th International Conference on Computational Methods in Systems Biology Rostock, Germany Springer pp. 176-190.
50. Hines M, Eichner H, Schuermann F, (2008) Neuron splitting in compute-bound parallel network simulations enables runtime scaling with twice as many processors. Journal of Computational Neuroscience 25: 203-210.
51. Howell F, Dyhrfjeld-Johnsen J, Maex R, Goddard N, De Schutter E, (2000) A large-scale network model of the cerebellar cortex using PGENESIS. Neurocomputing 32: 1041-1046.
52. Cornelis H, De Schutter E, (2007) Neurospaces: Towards automated model partitioning for parallel computers. Neurocomputing 70: 2117-2121.
53. A H, A H, (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology 117: 500-544.
54. Hines M, Morse T, Migliore M, Carnevale N, Shepherd G, (2004) ModelDB: A database to support computational neuroscience. Journal of Computational Neuroscience 17: 7-11.
55. Brooks FJ, (1995) The tar pit. pp. 3-6 Addison-Wesley Professional, 20th anniversary edition.
56. DeRemer F, Kron H, (1975) Programming-in-the large versus programming-in-the-small. In: Shooman M, Yeh R, editors. Proceedings of the International Conference on Reliable Software IEEE pp. 114-121.
57. Merriam-Webster Merriam-Webster Collegiate Dictionary. URL http://www.merriam-webster.com/. Accessed 2011 Dec 11.
58. IEEE ArchitectureWorking Group (2000) IEEE recommended practice for architectural description of software-intensive systems (IEEE Std 1471-2000).
59. Sutton S Jr, Rouvellou I, Issues in the design and implementation of a concern-space modeling schema. URL http://www.research.ibm.com/hyperspace/workshops/icse2001/Papers/sutton.pdf. Accessed 2011 Dec 11.
60. Sutton S Jr, Rouvellou I, (2004) Concern modeling for aspect-oriented software development. In: Filman R, Elrad T, Clarke S, Akit M, editors. Aspect-Oriented Software Development Boston Addison-Wesley pp. 479-505.
61. Dijkstra E, (1976) A Discipline of Programming Englewood Cliffs, NJ Prentice Hall.
62. Parnas D, (1972) On the criteria to be used in decomposing systems into modules. Communications of the Association of Computing Machinery 15: 1053-1058.