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Database Analysis of Simulated and Recorded Electrophysiological Datasets with PANDORA’s Toolbox

Neuroinformatics volume 7, pages 93–111 (2009)Cite this article

Abstract

Neuronal recordings and computer simulations produce ever growing amounts of data, impeding conventional analysis methods from keeping pace. Such large datasets can be automatically analyzed by taking advantage of the well-established relational database paradigm. Raw electrophysiology data can be entered into a database by extracting its interesting characteristics (e.g., firing rate). Compared to storing the raw data directly, this database representation is several orders of magnitude higher efficient in storage space and processing time. Using two large electrophysiology recording and simulation datasets, we demonstrate that the database can be queried, transformed and analyzed. This process is relatively simple and easy to learn because it takes place entirely in Matlab, using our database analysis toolbox, PANDORA. It is capable of acquiring data from common recording and simulation platforms and exchanging data with external database engines and other analysis toolboxes, which make analysis simpler and highly interoperable. PANDORA is available to be freely used and modified because it is open-source (http://software.incf.org/software/pandora/home).

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References

  • Achard, P., & Schutter, E. D. (2006). Complex parameter landscape for a complex neuron model. PLoS Computers in Biology, 2(7), e94. doi:10.1371/journal.pcbi.0020094.

    Article  Google Scholar 

  • Baxter, S. M., Day, S. W., Fetrow, J. S., & Reisinger, S. J. (2006). Scientific software development is not an oxymoron. PLoS Computers in Biology, 2, 975–978.

    CAS  Google Scholar 

  • Bean, B. P. (2007). The action potential in mammalian central neurons. Nature Reviews. Neuroscience, 8(6), 451–465. doi:10.1038/nrn2148.

    PubMed  Article  CAS  Google Scholar 

  • Bjaalie, J. (2008). Understanding the brain through neuroinformatics. Front Neuroscience, 2(1), 19–21. doi:10.3389/neuro.01.022.2008.

    Article  Google Scholar 

  • Bjaalie, J. G., & Grillner, S. (2007). Global neuroinformatics: The international neuroinformatics coordinating facility. Journal of Neuroscience, 27(14), 3613–3615. doi:10.1523/jneurosci.0558-07.2007.

    PubMed  Article  CAS  Google Scholar 

  • Bokil, H., Tchernichovski, O., & Mitra, P. P. (2006). Dynamic phenotypes: Time series analysis techniques for characterizing neuronal and behavioral dynamics. Neuroinformatics, 4(1), 119–128.

    PubMed  Article  Google Scholar 

  • Bower, J. M., & Beeman, D. (1998). The book of GENESIS (2nd ed.). New York: Springer.

    Google Scholar 

  • Brown, E. N., Kass, R. E., & Mitra, P. P. (2004). Multiple neural spike train data analysis: State-of-the-art and future challenges. Nature Neuroscience, 7(5), 456–461.

    PubMed  Article  CAS  Google Scholar 

  • Calin-Jageman, R. J., Tunstall, M. J., Mensh, B. D., Katz, P. S., & Frost, W. N. (2007). Parameter space analysis suggests multi-site plasticity contributes to motor pattern initiation in Tritonia. Journal of Neurophysiology, 98(4), 2382–2398. doi:10.1152/jn.00572.2007.

    PubMed  Article  Google Scholar 

  • Cannon, R. C., Gewaltig, M. O., Gleeson, P., Bhalla, U. S., Cornelis, H., Hines, M. L., et al. (2007) Interoperability of neuroscience modeling software: Current status and future directions. Neuroinformatics, 5(2), 127–138. doi:10.1007/s12021-007-0004-5.

    PubMed  Article  Google Scholar 

  • Carnevale, N., & Hines, M. (2006). The NEURON book. Cambridge: Cambridge University Press.

    Google Scholar 

  • Chamberlin, D. D., & Boyce, R. F. (1974). SEQUEL: A structured English query language. In International conference on management of data, proceedings of the 1974 ACM SIGFIDET (now SIGMOD) workshop on data description, access and control (pp. 249–264). Ann Arbor, Michigan.

  • Codd, E. (1970). A relational model of data for large shared data banks. Communications of the ACM, 13(6), 377–387.

    Article  Google Scholar 

  • Cui, J., Xu, L., Bressler, S. L., Ding, M., & Liang, H. (2008). BSMART: A MATLAB/C toolbox for analysis of multichannel neural time series. Neural Networks, 21(8, Sp. Iss. SI), 1094–1104. doi:10.1016/j.neunet.2008.05.007.

    PubMed  Article  Google Scholar 

  • Eaton, J. W. (2002). GNU Octave. A numerical engineering software package. http://www.che.wisc.edu/octave.

  • Elmasri, R., & Navathe, S. B. (1994). Fundamentals of database systems (2nd ed.). Reading: Addison-Wesley.

    Google Scholar 

  • Fonseca, C. M., & Fleming, P. J. (1993). Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In Genetic algorithms: Proceedings of the fifth international conference (pp. 416–423). San Francisco: Morgan Kaufmann.

    Google Scholar 

  • Gardner, D., Toga, A., Ascoli, G., Beatty, J., Brinkley, J., Dale, A., et al. (2003). Towards effective and rewarding data sharing. Neuroinformatics, 1(3), 289–295.

    PubMed  Article  Google Scholar 

  • Gardner, D., Akil, H., Ascoli, G. A., Bowden, D. M., Bug, W., Donohue, D. E., et al. (2008). The neuroscience information framework: A data and knowledge environment for neuroscience. Neuroinformatics, 6(3), 149–160. doi:10.1007/s12021-008-9024-z.

    PubMed  Article  Google Scholar 

  • Gleeson, P., Steuber, V., & Silver, R. A. (2007). neuroConstruct: A tool for modeling networks of neurons in 3D space. Neuron, 54(2), 219–235. doi:10.1016/j.neuron.2007.03.025.

    PubMed  Article  CAS  Google Scholar 

  • Günay, C. (2007). Plotting and analysis for neural database-oriented research applications (PANDORA) toolbox. http://userwww.service.emory.edu/~cgunay/pandora.

  • Günay, C. (2008a). PANDORA neural analysis toolbox. In Intenational Neuroinformatics coordinating facility (INCF) software center. http://software.incf.org/software/44/view/PANDORA.

  • Günay, C. (2008b). PANDORA neural analysis toolbox. SimToolDB. http://senselab.med.yale.edu/SimToolDB.

  • Günay, C., Edgerton, J. R., & Jaeger, D. (2008a). Channel density distributions explain spiking variability in the globus pallidus: A combined physiology and computer simulation database approach. Journal of Neuroscience, 28(30), 7476–7491. doi:10.1523/jneurosci.4198-07.2008.

    PubMed  Article  Google Scholar 

  • Günay, C., Hooper, R. M., Hammett, K. R., & Prinz, A. A. (2008b). Calcium sensor properties for activity-dependent homeostatic regulation of pyloric network rhythms in the lobster stomatogastric ganglion. BMC Neuroscience, 9(Suppl 1), P42.

    Article  Google Scholar 

  • Günay, C., Smolinski, T., Lytton, W., et al. (2008c). Computational intelligence in electrophysiology: Trends and open problems. In T. Smolinski, M. Milanova, & A. E. Hassanien (Eds.), Applications of computational intelligence in biology: Current trends and open problems (chap. XIV, pp. 325–359). New York: Springer.

    Google Scholar 

  • Herz, A. V., Meier, R., Nawrot, M. P., Schiegel, W., & Zito, T. (2008). G-Node: An integrated tool-sharing platform to support cellular and systems neurophysiology in the age of global neuroinformatics. Neural Networks, 21(8), 1070–1075. doi:10.1016/j.neunet.2008.05.011 (Special Issue on Neuroinformatics).

    PubMed  Article  Google Scholar 

  • Hines, M., Morse, T., Migliore, M., Carnevale, N., & Shepherd, G. (2004). ModelDB: A database to support computational neuroscience. Journal of Computational Neuroscience, 17(1), 7–11. http://senselab.med.yale.edu/ModelDB.

    PubMed  Article  Google Scholar 

  • Hodgkin, A., & Huxley, A. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, 117(4), 500–544.

    PubMed  CAS  Google Scholar 

  • Huys, Q. J. M., Ahrens, M. B., & Paninski, L. (2006). Efficient estimation of detailed single-neuron models. Journal of Neurophysiology, 96(2), 872–890. doi:10.1152/jn.00079.2006.

    PubMed  Article  Google Scholar 

  • Johnson, D. H., & Sinanović, S. (2001). Symmetrizing the Kullback-Leibler distance. Tech. Rep., Electrical & Computer Engineering Department, MS380 Rice University Houston, Texas 77005-1892. http://www-dsp.rice.edu/~dhj/resistor.pdf.

  • Kullback, S., & Leibler, R. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22(1), 79–86.

    Article  Google Scholar 

  • Lidierth, M. (2009). sigTOOL: A MATLAB-based environment for sharing laboratory-developed software to analyze biological signals. Journal of Neuroscience Methods. doi:10.1016/j.jneumeth.2008.11.004.

    PubMed  Google Scholar 

  • Liu, Z., Golowasch, J., Marder, E., & Abbott, L. F. (1998). A model neuron with activity-dependent conductances regulated by multiple calcium sensors. Journal of Neuroscience, 18(7), 309–320.

    Google Scholar 

  • Lytton, W. W. (2006). Neural query system—data-mining from within the neuron simulator. Neuroinformatics, 4(2), 163–175.

    PubMed  Article  Google Scholar 

  • Meier, R., Boven, K., Aertsen, A., & Egert, U. (2007). FIND—finding information in neural data, An open-source analysis toolbox for multiple-neuron recordings and network simulations. In Proc. 7th German Neurosci Meeting (p. 1212).

  • Meier, R., Egert, U., Aertsen, A., & Nawrot, M. P. (2008). FIND—A unified framework for neural data analysis. Neural Networks, 21(8), 1085–1093. doi:10.1016/j.neunet.2008.06.019 (Special Issue on Neuroinformatics).

    PubMed  Article  Google Scholar 

  • Morse, T. (2007). Model sharing in computational neuroscience. Scholarpedia, 2(4), 3036. http://www.scholarpedia.org/article/Model_sharing_in_computational_neuroscience.

    Google Scholar 

  • Nicolelis, M., Dimitrov, D., Carmena, J., Crist, R., Lehew, G., Kralik, J., et al. (2003). Chronic, multisite, multielectrode recordings in macaque monkeys. Proceedings of the National Academy of Sciences of the United States of America, 100(19), 11041–11046.

    PubMed  Article  CAS  Google Scholar 

  • Pittendrigh, S., & Jacobs G. (2003). Neurosys: A semistructured laboratory database. Neuroinformatics, 1(2), 167–176.

    PubMed  Article  Google Scholar 

  • Prinz, A. A., Billimoria, C. P., & Marder, E. (2003). Alternative to hand-tuning conductance-based models: Construction and analysis of databases of model neurons. Journal of Neurophysiology, 90, 3998–4015.

    PubMed  Article  Google Scholar 

  • Prinz, A. A., Bucher, D., & Marder, E. (2004). Similar network activity from disparate circuit parameters. Nature Neuroscience, 7(12), 1345–1352.

    PubMed  Article  CAS  Google Scholar 

  • Sekerli, M., Del Negro, C., Lee, R., & Butera, R. (2004). Estimating action potential thresholds from neuronal time-series: New metrics and evaluation of methodologies. IEEE Transactions on Biomedical Engineering, 51(9), 1665–1672. doi:10.1109/TBME.2004.827531.

    PubMed  Article  Google Scholar 

  • Shepherd, G., Mirsky, J., Healy, M., et al. (1998). The human brain project: Neuroinformatics tools for integrating, searching and modeling multidisciplinary neuroscience data. TINS, 21(11).

  • Sinanović, S., & Johnson, D. H. (2007). Toward a theory of information processing. Signal Processing, 87, 1326–1344.

    Article  Google Scholar 

  • Smolinski, T. G., Prinz, A. A., & Zurada, J. M. (2008). Hybridization of rough sets and multi-objective evolutionary algorithms for classificatory signal decomposition. In A. E. Hassanien, Z. Suraj, D. Ślȩzak, & P. Lingras (Eds.), Rough computing: Theories, technologies, and applications (chap. X, pp. 204–227). Hershey: Information Science Reference.

    Google Scholar 

  • Taylor, A. L., Hickey, T. J., Prinz, A. A., & Marder, E. (2006). Structure and visualization of high-dimensional conductance spaces. Journal of Neurophysiology, 96, 891–905.

    PubMed  Article  Google Scholar 

  • Van Geit, W., Achard, P., & Schutter, E. D. (2007). Neurofitter: A parameter tuning package for a wide range of electrophysiological neuron models. Frontiers in Neuroinformatics, 1, 1.

  • Van Geit, W., De Schutter, E., & Achard, P. (2008). Automated neuron model optimization techniques: A review. Biological Cybernetics, 99(4–5), 241–251. doi:10.1007/s00422-008-0257-6.

    PubMed  Article  Google Scholar 

  • Vanier, M. C., & Bower, J. M. (1999). A comparative survey of automated parameter-search methods for compartmental neural models. Journal of Computational Neuroscience, 7, 149–171.

    PubMed  Article  CAS  Google Scholar 

  • Weaver, C., & Wearne, S. (2006). The role of action potential shape and parameter constraints in optimization of compartment models. Neurocomputing, 69(10–12), 1053–1057. doi:10.1016/j.neucom.2005.12.044 (14th Annual Computational Neuroscience Meeting (CNS 05), Madison, WI, 17–21 July, 2005).

    Article  Google Scholar 

  • Wood, R., Gurney, K., & Wilson, C. (2004). A novel parameter optimisation technique for compartmental models applied to a model of a striatal medium spiny neuron. Neurocomputing, 58, 1109–1116. doi:10.1016/j.neucom.2004.01.174.

    Article  Google Scholar 

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Acknowledgements

This project is supported by NINDS R01-NS039852 and NIMH R01-MH065634 awarded to D. Jaeger and 1 R01 NS054911-01A1 from NINDS awarded to A. Prinz. The PANDORA Toolbox includes code from Alfonso Delgado-Reyes, for which the authors are grateful. Special thanks to Horatiu Voicu, Eric Hendrickson, Robert Clewley and Kelly Suter for providing helpful comments and suggestions to earlier drafts of this paper; and to Natalia Toporikova for providing test data and testing initial versions of PANDORA. We thank both anonymous reviewers, whose comments dramatically improved the manuscript and the time performance of indexing operations in PANDORA.

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Authors and Affiliations

  1. Dept. of Biology, Emory University, 1510 Clifton Rd., Atlanta, GA, 30322, USA

    Cengiz Günay, Jeremy R. Edgerton, Astrid A. Prinz & Dieter Jaeger

  2. Neurosurgery Department, School of Medicine, Emory University, Atlanta, GA, 30322, USA

    Su Li

  3. Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Okinawa, Japan

    Thomas Sangrey

Authors
  1. Cengiz Günay
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  2. Jeremy R. Edgerton
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  3. Su Li
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  6. Dieter Jaeger
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Correspondence to Cengiz Günay.

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Günay, C., Edgerton, J.R., Li, S. et al. Database Analysis of Simulated and Recorded Electrophysiological Datasets with PANDORA’s Toolbox. Neuroinform 7, 93–111 (2009). https://doi.org/10.1007/s12021-009-9048-z

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  • DOI: https://doi.org/10.1007/s12021-009-9048-z

Keywords

  • Database
  • Data visualization
  • Matlab
  • Neural model
  • Simulation
  • Electrophysiology
  • SQL
  • Large datasets
  • Automated analysis
  • Pandora
  • Open-source