Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

Neuronal Model Databases

Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7320-6_165-1

Synonyms

Definition

A neuronal model database, in contrast to neuronal databases that collect experimental data, holds instances of computational models of one type. This model can be of a single neuron or a neuronal network, which is replicated by varying its input model parameters to yield many instances that are inserted into a searchable database. Each entry in the database corresponds to one model instance, which contains: (1) values of the varied parameters (e.g., maximal conductance, reversal potential, synaptic weights) required to uniquely identify and sufficient to re-simulate the model; and (2) several key output characteristics from the model simulation (e.g., firing rate for a single neuron or a bursting period in a network). The resulting database is often used to study the relevant properties (response to stimulus or firing activity characteristics) of the model across...

Keywords

Marin 
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References

  1. Ball JM, Franklin CC, Tobin AE, Schulz DJ, Nair SS (2010) Coregulation of ion channel conductances preserves output in a computational model of a crustacean cardiac motor neuron. J Neurosci 30(25):8637–8649. http://www.jneurosci.org/content/30/25/8637.short
  2. Bhalla US, Bower JM (1993) Exploring parameter space in detailed single neuron models: simulations of the mitral and granule cells of the olfactory bulb. J Neurophysiol 69(6):1948–1965PubMedGoogle Scholar
  3. Calin-Jageman RJ, Katz PS (2006) A distributed computing tool for generating neural simulation databases. Neural Comput 18(12):2923–2927. doi:10.1162/neco.2006.18.12.2923. http://dx.doi.org/10.1162/neco.2006.18.12.2923
  4. Calin-Jageman RJ, Tunstall MJ, Mensh BD, Katz PS, Frost WN (2007) Parameter space analysis suggests multi-site plasticity contributes to motor pattern initiation in Tritonia. J Neurophysiol 98(4):2382–2398. doi:10.1152/jn.00572.2007, ISSN 0022–3077PubMedCrossRefGoogle Scholar
  5. DeSchutter E, Bower J (1994) An active membrane model of the cerebellar purkinje-cell. 1. Simulation of current clamps in slice. J Neurophysiol 71(1):375–400, ISSN 0022–3077Google Scholar
  6. Doloc-Mihu A, Calabrese RL (2011). A database of computational models of a half-center oscillator for analyzing how neuronal parameters influence network activity. J Biol Phys 37(3):263–283. doi:10.1007/s10867-011-9215-y. http://dx.doi.org/10.1007/s10867-011-9215-y
  7. Foster W, Ungar L, Schwaber J (1993) Significance of conductances in Hodgkin-Huxley models. J Neurophysiol 70(6):2502–2518, ISSN 0022–3077PubMedGoogle Scholar
  8. Goldman M, Golowasch J, Marder E, Abbott L (2001) Global structure, robustness, and modulation of neuronal networks. J Neurosci 21:5229–5238PubMedGoogle Scholar
  9. Günay C, Prinz AA (2009) Finding sensors for homeostasis of biological neuronal networks using artificial neural networks. In: Kozma R, Venayagamoorthy GK (eds) Proceedings of the 2009 international joint conference on neural networks (IJCNN), IEEE Computer Society, Los Alamitos, pp 1025–1032. ISBN 978-1-4244-3548-7. doi:10.1109/IJCNN.2009.5178991Google Scholar
  10. Günay C, Prinz AA (2010) Model calcium sensors for network homeostasis: sensor and readout parameter analysis from a database of model neuronal networks. J Neurosci 30:1686–1698. doi:10.1523/jneurosci.3098-09.2010, NIHMS176368, PMC2851246PubMedCentralPubMedCrossRefGoogle Scholar
  11. Günay C, Edgerton JR, Jaeger D (2008) Channel density distributions explain spiking variability in the globus pallidus: a combined physiology and computer simulation database approach. J Neurosci 28(30):7476–7491. doi:10.1523/jneurosci.4198-07.2008PubMedCrossRefGoogle Scholar
  12. Günay C, Edgerton JR, Li S, Sangrey T, Prinz AA, Jaeger D (2009) Database analysis of simulated and recorded electrophysiological datasets with PANDORA’s toolbox. Neuroinformatics 7(2):93–111. doi:10.1007/s12021-009-9048-zPubMedCentralPubMedCrossRefGoogle Scholar
  13. Hudson AE, Prinz AA (2010) Conductance ratios and cellular identity. PLoS Comput Biol 6(7):e1000838. doi:10.1371/journal.pcbi.1000838. http://dx.doi.org/10.1371/journal.pcbi.1000838
  14. Marin B, Barnett WH, Doloc-Mihu A, Calabrese RL, Cymbalyuk GS (2013) High prevalence of multistability of rest states and bursting in a database of a model neuron. PLoS Comput Biol 9(3):e1002930. doi:10.1371/journal.pcbi.1002930. http://dx.doi.org/10.1371/journal.pcbi.1002930
  15. Nowotny T, Szücs A, Levi R, Selverston AI (2007) Models wagging the dog: are circuits constructed with disparate parameters? Neural Comput 19(8): 1985–2003. doi:10.1162/neco.2007.19.8.1985. http://dx.doi.org/10.1162/neco.2007.19.8.1985
  16. Opitz D, Maclin R (1997) An empirical evaluation of bagging and boosting for artificial neural networks. In: Neural networks, 1997, international conference on, vol 3, IEEE, pp 1401–1405. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=613999
  17. Prinz AA (2010) Computational approaches to neuronal network analysis. Philos Trans R Soc Lond B Biol Sci 365(1551):2397–2405. doi:10.1098/rstb.2010.0029. http://dx.doi.org/10.1098/rstb.2010.0029
  18. Prinz AA, Billimoria CP, Marder E (2003) Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. J Neurophysiol 90:3998–4015PubMedCrossRefGoogle Scholar
  19. Prinz AA, Bucher D, Marder E (2004) Similar network activity from disparate circuit parameters. Nat Neurosci 7(12):1345–1352PubMedCrossRefGoogle Scholar
  20. Soofi W, Archila S, Prinz AA (2012) Co-variation of ionic conductances supports phase maintenance in stomatogastric neurons. J Comput Neurosci 33(1):77–95. doi:10.1007/s10827-011-0375-3. http://dx.doi.org/10.1007/s10827-011-0375-3
  21. Strogatz SH (2006) Nonlinear dynamics and chaos (with applications to physics, biology, chemistry, and Engineering). Perseus Publishing, New YorkGoogle Scholar
  22. Williams AH, Kwiatkowski MA, Mortimer AL, Marder E, Zeeman ML, Dickinson PS (2013) Animal-to-animal variability in the phasing of the crustacean cardiac motor pattern: an experimental and computational analysis. J Neurophysiol 109(10):2451–2465. doi:10.1152/jn.01010.2012. http://dx.doi.org/10.1152/jn.01010.2012

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Department of BiologyEmory UniversityAtlantaUSA