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An Automated System for Simulating Multi-Compartmenal Models and its Application to Investigate Morphological Properties of a Neuronal Coincidence Detector

Michael Stransky1, Thomas Wachtler2, Hans J. Ohlbach1 and Philipp L. Rautenberg2*
  • 1 Ludwig-Maximilians-Universität München, Institute for Computer Science, Germany
  • 2 Ludwig-Maximilians-Universität München, Department of Biology, Germany

Neuronal morphology plays a fundamental role for the information processing capabilities of neurons. These dependencies can be investigated using compartmental models to simulate functional properties of morphologically characterized neurons. We developed an automated system to investigate the effect of realistic morphologies on the electrophysiological properties of a neuron and applied it to morphologies of neurons of the medial superior olive (MSO) from published data (Rautenberg et al., 2009). The simulation paradigm was designed to be in accordance with electrophysiological experiments conducted by Scott et al. (2005), concerning measurements of input resistance and effective membrane time constant. Simulating neurons from morphological data using compartmental models implies a highly complex parameter space, both in terms of the simulation parameters and the results. To cope with this complexity, we used an integrative approach that employs a database to manage the data, control the simulations, and analyze the results. This yields the requisite for efficient data analysis: Morphologies can be easily exchanged and thus a great variety of analyzes can be performed in a systematic and non-redundant fashion. Even patch-clamp-experiments can be simulated within the database, eliminating the need of performing the same analyses "by hand" again and again for each morphology. This in turn facilitates analysis and visualization of results, and the results are presented in a portable and easy way via direct database access and web interface. For simulating neurons we used the NEURON simulation software (Hines and Carnevale, 1997. Simulations were performed using database and computing services at the German INCF Node (www.g-node.org). Data were stored and simulations were controlled via a PostgreSQL Database. PostgreSQL easily allows to incorporate Python code using plpython, so we could integrate NEURON via Python for simulations and libraries like MDP (Zito et al., 2008) for analysis. In this manner, we performed automated simulations with different morphologies within the database. In addition to the detailed simulation of reconstructed MSO neurons, we performed simulations of simplified models, where the morphology was reduced to three compartments using passive parameters that were estimated with the complex models, like specific axial resistivity constant. Comparison of the simulated responses of the simple model to responses of the morphological complex models enabled us to relate the geometrical properties to functional properties of the neurons. For example, we found that surface area has to change when simplifying the morphology while keeping electro-physiological properties like input resistance constant. Our work demonstrates the efficiency of a unifying approach employing a database for management of both data and analysis tools. Acknowledgments: The German INCF Node is supported by BMBF grant 01GQ0801

References

1. Hines, M. L., & Carnevale, N. T. (1997). The NEURON simulation environment. Neural Computation, 9(6), 1179-1209.

2. Rautenberg, P. L., Grothe, B., & Felmy, F. (2009). Quantification of the three-dimensional morphology of coincidence detector neurons in the medial superior olive of gerbils during late postnatal development. The Journal of Comparative Neurology, 517(3), 385-396. doi:10.1002/cne.22166

3. Scott, L. L., Mathews, P. J., & Golding, N. L. (2005). Posthearing developmental refinement of temporal processing in principal neurons of the medial superior olive. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 25(35), 7887-7895. doi:10.1523/JNEUROSCI.1016-05.2005

4. Zito, T., Wilbert, N., Wiskott, L., & Berkes, P. (2008). Modular Toolkit for Data Processing (MDP): A Python Data Processing Framework. Frontiers in Neuroinformatics, 2, 8. doi:10.3389/neuro.11.008.2008

Conference: Neuroinformatics 2010 , Kobe, Japan, 30 Aug - 1 Sep, 2010.

Presentation Type: Poster Presentation

Topic: Computational Neuroscience

Citation: Stransky M, Wachtler T, Ohlbach HJ and Rautenberg PL (2010). An Automated System for Simulating Multi-Compartmenal Models and its Application to Investigate Morphological Properties of a Neuronal Coincidence Detector. Front. Neurosci. Conference Abstract: Neuroinformatics 2010 . doi: 10.3389/conf.fnins.2010.13.00088

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Received: 14 Jun 2010; Published Online: 14 Jun 2010.

* Correspondence: Philipp L Rautenberg, Ludwig-Maximilians-Universität München, Department of Biology, Planegg-Martinsried, Germany, philipp.rautenberg@gmail.com