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

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).
• 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.