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Related Experiment Videos

CoCoDat: a database system for organizing and selecting quantitative data on single neurons and neuronal

J Dyhrfjeld-Johnsen1, J Maier, D Schubert

  • 1C. and O. Vogt Brain Research Institute, Heinrich Heine University Düsseldorf, Moorenstr. 5, D-40225 Düsseldorf, Germany.

Journal of Neuroscience Methods
|January 22, 2005
PubMed
Summary

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We developed CoCoDat, a novel database system for organizing and retrieving quantitative neuroscientific data on single neurons and microcircuitry. This system aids researchers in experimental planning and computational modeling by providing flexible data access.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biophysics

Background:

  • Existing neuroscientific databases struggle with the diversity and method-dependence of single-cell and microcircuitry data.
  • A need exists for systems that allow data entry and retrieval without a priori interpretation or summarization.

Purpose of the Study:

  • To present a novel database system, CoCoDat, for organizing and selecting quantitative experimental data on single neurons and neuronal microcircuitry.
  • To provide tools for reference-keeping, experimental planning, and computational modeling.

Main Methods:

  • Developed a database system based on biophysical theory for flexible data representation (membrane conductances, ionic/synaptic currents, morphology, connectivity, firing patterns).
  • Implemented innovative data retrieval tools with optional relaxation of search criteria (brain region, cortical layer, cell type, subcellular compartment).

Related Experiment Videos

  • Demonstrated the system's utility in constructing, tuning, and validating a multicompartmental model of a rat barrel cortex pyramidal cell.

    • Main Results:

    • CoCoDat effectively organizes and retrieves diverse neuroscientific data, accommodating method-dependence.
    • Innovative search relaxation tools overcome the trade-off between data fidelity and retrieval convenience.
    • The system facilitated the creation and validation of a complex neuronal model.

    Conclusions:

    • CoCoDat offers a scalable solution for managing quantitative neuroscientific data, from individual researchers to distributed networks.
    • The system's flexible data representation and retrieval tools enhance its utility for computational neuroscience and experimental planning.
    • CoCoDat is freely available and supports platform-independent data sharing via XML format.