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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

Abstract

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.

Keywords

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

Notes

Acknowledgement

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

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of BiologyEmory UniversityAtlantaUSA

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