Journal of Biological Physics

, Volume 37, Issue 3, pp 263–283 | Cite as

A database of computational models of a half-center oscillator for analyzing how neuronal parameters influence network activity

  • Anca Doloc-MihuEmail author
  • Ronald L. Calabrese
Original Paper


A half-center oscillator (HCO) is a common circuit building block of central pattern generator networks that produce rhythmic motor patterns in animals. Here we constructed an efficient relational database table with the resulting characteristics of the Hill et al.’s (J Comput Neurosci 10:281–302, 2001) HCO simple conductance-based model. The model consists of two reciprocally inhibitory neurons and replicates the electrical activity of the oscillator interneurons of the leech heartbeat central pattern generator under a variety of experimental conditions. Our long-range goal is to understand how this basic circuit building block produces functional activity under a variety of parameter regimes and how different parameter regimes influence stability and modulatability. By using the latest developments in computer technology, we simulated and stored large amounts of data (on the order of terabytes). We systematically explored the parameter space of the HCO and corresponding isolated neuron models using a brute-force approach. We varied a set of selected parameters (maximal conductance of intrinsic and synaptic currents) in all combinations, resulting in about 10 million simulations. We classified these HCO and isolated neuron model simulations by their activity characteristics into identifiable groups and quantified their prevalence. By querying the database, we compared the activity characteristics of the identified groups of our simulated HCO models with those of our simulated isolated neuron models and found that regularly bursting neurons compose only a small minority of functional HCO models; the vast majority was composed of spiking neurons.


Bursting Oscillation Central pattern generator Database Parameter variation Simulation Isolated neuron Automated analysis Large datasets 



This work was supported by the National Institute of Neurological Disorders and Stroke Grant NS024072 to R.L. Calabrese.


  1. 1.
    Hill, A.A.V., Lu, J., Masino, M.A., Olsen, O.H., Calabrese, R.L.: A model of a segmental oscillator in the leech heartbeat neuronal network. J. Comput. Neurosci. 10, 281–302 (2001)CrossRefGoogle Scholar
  2. 2.
    Kristan, W.B. Jr., Calabrese, R.L., Friesen, W.O.: Neuronal control of leech behavior. Progr. Neurobiol. 76(5), 279–327 (2005)CrossRefGoogle Scholar
  3. 3.
    Marder, E., Calabrese, R.L.: Principles of rhythmic motor pattern generation. Physiol. Rev. 76(3), 687–717 (2004)Google Scholar
  4. 4.
    Cymbalyuk, G.S., Calabrese, R.L.: Oscillatory behaviors in pharmacologically isolated heart interneurons from the medicinal leech. J. Neurocomput. 32–33, 97–104 (2000)CrossRefGoogle Scholar
  5. 5.
    Dean, J., Cruse, H.: Motor pattern generation. In: Arbib, M. (ed.) The Handbook of Brain Theory and Neural Networks, pp. 600–605. MIT, Cambridge (1995)Google Scholar
  6. 6.
    Marder, E., Bucher, D., Schulz, D.J., Taylor, A.L.: Invertebrate central pattern generation moves along. Curr. Biol. 15, R685–R699 (2005)CrossRefGoogle Scholar
  7. 7.
    Prinz, A.A., Bucher, D., Marder, E.: Similar network activity from disparate circuit parameters. Nature Neurosci. 7, 1345–1352 (2004)CrossRefGoogle Scholar
  8. 8.
    Olypher, A., Calabrese, R.L.: Using constraints on neuronal activity to reveal compensatory changes in neuronal parameters. J. Neurophysiol. 98, 3749–3758 (2007)CrossRefGoogle Scholar
  9. 9.
    Olypher, A., Calabrese, R.L.: How does maintenance of network activity depend on endogenous dynamics of isolated neurons? Neural Comput. 21, 1665–1682 (2009). Erratum in: Neural Comput. 21, 2405 (2009)Google Scholar
  10. 10.
    De Schutter, E., Ekeberg, O., Kotaleski, J.H., Achard, P., Lansner, A.: Biophysically detailed modelling of microcircuits and beyond. Trends Neurosci. 28, 562–569 (2005)CrossRefGoogle Scholar
  11. 11.
    Grillner, S., Kozlov, A., Dario, P., Stefanini, C., Menciassi, A., Lansner, A., Hellgren Kotaleski, J.: Modeling a vertebrate motor system: pattern generation, steering and control of body orientation. Prog. Brain Res. 165, 221–234 (2007)CrossRefGoogle Scholar
  12. 12.
    Prinz, Insights Insights from models of rhythmic motor systems. Curr. Opin. Neurobiol. 16(6), 615–620 (2006)CrossRefGoogle Scholar
  13. 13.
    Elmasri, R., Navathe, S.B.: Fundamentals of Database Systems, 2nd edn. Addison-Wesley, Menlo Park (1994)zbMATHGoogle Scholar
  14. 14.
    Codd, E.F.: A relational model of data for large shared data banks. Commun. ACM 13(6), 377–387 (1970)zbMATHCrossRefGoogle Scholar
  15. 15.
    Prinz, A.A., Billimoria, C.P., Marder, E.: Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. J. Neurophysiol. 90, 3998–4015 (2003)CrossRefGoogle Scholar
  16. 16.
    Calin-Jageman, R.J., Tunstall, M.J., Mensh, B.D., Katz, P.S., Frost, W.N.: Parameter space analysis suggests multi-site plasticity contributes to motor pattern initiation in tritonia. J. Neurophysiol. 98(4), 2382–2398 (2007)CrossRefGoogle Scholar
  17. 17.
    Gunay, C., Edgerton, J.R., Li, S., Sangrey, T., Prinz, A.A., Jaeger, D.: Database analysis of simulated and recorded electrophysiological datasets with PANDORA’s toolbox. Neuroinformatics 2(7), 93–111 (2009). CrossRefGoogle Scholar
  18. 18.
    Cymbalyuk, G.S., Calabrese, R.L.: A model of slow plateau-like oscillations based upon the fast Na+ current in a window mode. J. Neurocomput. 38, 159–166 (2001)CrossRefGoogle Scholar
  19. 19.
    Cymbalyuk, G.S., Gaudry, Q., Masino, M.A., Calabrese, R.L.: Bursting in leech heart interneurons: Cell-autonomous and network-based mechanisms. J. Neurosci. 22(24), 10580–10592 (2002)Google Scholar
  20. 20.
    Bhalla, U.S., Bower, J.M.: Exploring parameter space in detailed single neuron models: simulations of the mitral and granule cells of the olfactory bulb. J. Neurophysiol. 69(6), 1948–1965 (1993)Google Scholar
  21. 21.
    Gunay, C., Edgerton, J.R., Jaeger, D.: Channel density distributions explain spiking variability in the globus pallidus: a combined physiology and computer simulation database approach. J. Neurosci. 28, 7476–7491 (2008)CrossRefGoogle Scholar
  22. 22.
    Wenning, A., Cymbalyuk, G.S., Calabrese, R.: Heartbeat control in leeches. I. Constriction pattern and neural modulation of blood pressure in intact animals. J. Neurophysiol. 91, 382–396 (2004)CrossRefGoogle Scholar
  23. 23.
    Calabrese, R.L.: The neural control of alternate heartbeat coordination states in the leech. J. Comp. Physiol. 122, 111–143 (1977)CrossRefGoogle Scholar
  24. 24.
    Hodgkin, A.L., Huxley, A.F.: A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117, 500–544 (1952)Google Scholar
  25. 25.
    Bower, J.M., Beeman, D.: The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System, 2nd edn., Springer (TELOS), New York (1998). Google Scholar
  26. 26.
    Wikipedia: Brute-Force Search (2010).
  27. 27.
    Wikipedia: Free Lossless Audio Codec (2010). Http://
  28. 28.
  29. 29.
    Marder, E., Goaillard, J.M.: Variability, compensation and homeostasis in neuron and network function. Nat. Rev. Neurosci. 7, 563–574 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of BiologyEmory UniversityAtlantaUSA

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