libNeuroML and PyLEMS: Using Python to combine procedural and declarative modeling approaches in computational neuroscience

Frontiers in Neuroinformatics

Volumn 8, Issue APR, 2014, Pages

  Vella, Michael ^a   Cannon, Robert C ^b   Crook, Sharon ^c   Davison, Andrew P ^d   Ganapathy, Gautham ^b   Robinson, Hugh P C ^a   Angus Silver, R ^e   Gleeson, Padraig ^e  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b Textensor Limited   (United Kingdom)

^c ARIZONA STATE UNIVERSITY   (United States)

^d Unite de Neurosciences Integratives et Computationnelles   (France)

^e UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

API; LEMS; Model specification; Modeling; NeuroML; Python; Standardization; SWC

Indexed keywords

ARTICLE; BEHAVIOR; COMPUTATIONAL NEUROSCIENCE; COMPUTER LANGUAGE; COMPUTER MODEL; COMPUTER PROGRAM; MORPHOLOGY; NEUROSCIENCE; PYTHON; SYSTEMS BIOLOGY; VALIDATION PROCESS;

EID: 84899133456     PISSN: None     EISSN: 16625196     Source Type: Journal    
DOI: 10.3389/fninf.2014.00038     Document Type: Article

References (26)

1. Alivisatos, A. P., Chun, M., Church, G. M., Greenspan, R. J., Roukes, M. L., and Yuste, R. (2012). The brain activity map project and the challenge of functional connectomics. Neuron 74, 970-974. doi: 10.1016/j.neuron.2012.06.006
2. Ascoli, G. A., Donohue, D. E., and Halavi, M. (2007). NeuroMorpho. org: a central resource for neuronal morphologies. J. Neurosci. 27, 9247-9251. doi: 10.1523/JNEUROSCI.2055-07.2007
3. Bornstein, B. J., Keating, S. M., Jouraku, A., and Hucka, M. (2008). LibSBML: an API Library for SBML. Bioinformatics 24, 880-881. doi: 10.1093/bioinformatics/btn051
4. Brette, R., and Gerstner, W. (2005). Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J. Neurophysiol. 94, 3637-3642. doi: 10.1152/jn.00686.2005
5. Cannon, R., Gewaltig, M., Gleeson, P., Bhalla, U., Cornelis, H., Hines, M., et al. (2007). Interoperability of neuroscience modeling software: current status and future directions. Neuroinformatics 5, 127-138. doi: 10.1007/s12021-007-0004-5
6. Cannon, R., Gleeson, P., Crook, S., and Silver, R. A. (2012). A declarative model specification system allowing NeuroML to be extended with user-defined component types. BMC Neurosci. 13(Suppl. 1):P42. doi: 10.1186/1471-2202-13-S1-P42
7. Cannon, R. C., Wheal, H. V., and Turner, D. A. (1999). Dendrites of classes of hippocampal neurons differ in structural complexity and branching patterns. J. Comp. Neurol. 413, 619-633. doi: 10.1002/(SICI)1096-9861(19991101)413:4<619::AID-CNE10>3.0.CO;2-B
8. Carnevale, N. T., and Hines, M. L. (2006). The NEURON Book. Cambridge: Cambridge University Press.
9. Crook, S. M., Plesser, H. E., and Davison, A. P. (2013). "Learning from the past: approaches for reproducibility in computational neuroscience, " in 20 Years of Computational Neuroscience, ed J. M. Bower (New York, NY: Springer), 73-102.
10. Davison, A. P., Brüderle, D., Eppler, J., Kremkow, J., Muller, E., Pecevski, D. A., et al. (2009). PyNN: a common interface for neuronal network simulators. Front. Neuroinform. 2:11. doi: 10.3389/neuro.11.011.2008
11. Gewaltig, M.-O., and Diesmann, M. (2007). NEST. Scholarpedia 2:1430. doi: 10.4249/scholarpedia.1430
12. Gleeson, P., Crook, S., Cannon, R. C., Hines, M. L., Billings, G. O., Farinella, M., et al. (2010). NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail. PLoS Comp. Biol. 6:e1000815. doi: 10.1371/journal.pcbi.1000815
13. Gleeson, P., Steuber, V., and Silver, R. A. (2007). neuroConstruct: a tool for modeling networks of neurons in 3D space. Neuron 54, 219-235. doi: 10.1016/j.neuron.2007.03.025
14. Goddard, N., Hucka, M., Howell, F., Cornelis, H., Shankar, K., and Beeman, D. (2001). Towards NeuroML: model description methods for collaborative modeling in neuroscience. Philos. Trans. R. Soc. Lond. B Biol. Sci. 356, 1209-1228. doi: 10.1098/rstb.2001.0910
15. Goodman, D., and Brette, R. (2008). Brian: a simulator for spiking neural networks in Python. Front. Neuroinform. 2:5. doi: 10.3389/neuron.11.005.2008
16. Hindmarsh, J. L., and Rose, R. M. (1984). A model of neuronal bursting using three coupled first order differential equations. Proc. R. Soc. Lond. B Biol. Sci. 221, 87-102. doi: 10.1098/rspb.1984.0024
17. Hines, M., Davison, A., and Muller, E. (2009). NEURON and Python. Front. Neuroinform. 3:1. doi: 10.3389/neuro.11.001.2009
18. Hines, M. L., and Carnevale, N. T. (1997). The NEURON simulation environment. Neural Comput. 9, 1179-1209. doi: 10.1162/neco.1997.9.6.1179
19. Hodgkin, A. L., and Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117, 500-544.
20. Hucka, M., Finney, A., Sauro, H. M., Bolouri, H., Doyle, J. C., Kitano, H., et al. (2003). The Systems Biology Markup Language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524-531. doi: 10.1093/bioinformatics/btg015
21. Izhikevich, E. (2003). Simple model of spiking neurons. IEEE Trans. Neural Netw. 14, 1569-1572. doi: 10.1109/TNN.2003.820440
22. Markram, H. (2006). The Blue Brain Project. Nat. Rev. Neurosci. 7, 153-160. doi: 10.1038/nrn1848
23. Miner, R. (2005). The importance of MathML to mathematics communication. Not. AMS 52, 532-538.
24. Raikov, I., Cannon, R., Clewley, R., Cornelis, H., Davison, A. P., De Schutter, E., et al. (2011). NineML: the network interchange for neuroscience modeling language. BMC Neurosci. 12(Suppl. 1):P330. doi: 10.1186/1471-2202-12-S1-P330
25. Richmond, P., Cope, A., Gurney, K., and Allerton, D. (2013). From model specification to simulation of biologically constrained networks of spiking neurons. Neuroinformatics 1-17. doi: 10.1007/s12021-013-9208-z. [Epub ahead of print].
26. Sauro, H. M., and Bergmann, F. T. (2008). Standards and ontologies in computational systems biology. Essays Biochem. 45, 211-222. doi: 10.1042/BSE0450211