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

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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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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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