, Volume 4, Issue 2, pp 163–175 | Cite as

Neural query system

Data-mining from within the NEURON simulator
  • William W. Lytton
Original Article


We have developed a simulation tool within the NEURON simulator to assist in organization, verification, and analysis of simulations. This tool, denominated Neural Query System (NQS), provides a relational database system, a query function based on the SELECT function of Structured Query Language, and data-mining tools. We show how NQS can be used to organize, manage, verify, and visualize parameters for both single cell and network simulations. We demonstrate an additional use of NQS to organize simulation output and relate outputs to parameters in a network model. The NQS software package is available at http://senselab. *** DIRECT SUPPORT *** A11U5014 00003


Dendritic Tree Structure Query Language Branch Order Database Table Cell Table 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Ascoli, G., Krichmar, J., Scorcioni, R., Nasuto, S., and Senft, S. (2001a) Computer generation and quantitative morphometric analysis of virtual neuron. Anatomy Embryol. 204, 283–301.CrossRefGoogle Scholar
  2. Ascoli, G., Krichmar, J., Nasuto, S., and Senft, S. (2001b) Generation, description and storage of dendritic morphology data. Phil. Trans. R. Soc. Lond. B 356, 1131–1145.CrossRefGoogle Scholar
  3. Ascoli, G. (2002) Neuroanatomical algorithms for dendritic modelling. Network-Comput. Neural Syst. 13, 247–260.CrossRefGoogle Scholar
  4. Bazhenov, M. Timofeev, I., Steriade, M., and Sejnowski, T. (1998) Computational models of thalamocortical augmenting responses. J. Neurosci. 18, 6444–6465.Google Scholar
  5. Bower, J., and Beeman, D. (1998) The Book of Genesis, 2nd ed., Springer, New York.Google Scholar
  6. Chover, J., Haberly, L., and Lytton, W. (2001) Alternating dominance of NMDA and AMPA for learning and recall: a computer model. Neuroreport 12, 2503–2507.CrossRefGoogle Scholar
  7. Davison, A., Morse, T., Migliore, M., and Shepherd, G. (2004) Semi-automated population of an online database of neuronal models (ModelDB) with citation information, using PubMed for validation. Neuroinformatics 2, 327–332.CrossRefGoogle Scholar
  8. Galassi, M., Davies, J., Theiler, J., et al. (2003) Gnu Scientific Library: Reference Manual, 2nd ed., Network Theory, Cambridge, MA.Google Scholar
  9. Goddard, N., Hucka, M., Howell, F., Cornells, H., Shankar, K., and Beeman, D. (2001) Towards NeuroML: model description methods for collaborative modelling in neuroscience. Phil. Trans. R. Soc. Lond. B 356, 1209–1228.CrossRefGoogle Scholar
  10. Hines, M., Morse, T., Migliore, M., Carnevale, N., and Shepherd, G. (2004) Modelbd: a database to support computational neuroscience. J. Comput. Neurosci. 17, 73–77.CrossRefGoogle Scholar
  11. Lytton, W. (2002) From Computer to Brain Springer Verlag, New York.Google Scholar
  12. Poirazi, P., Brannon, T., and Mel, B. (2003a) Arithmetic of subthreshold synaptic summation in a model calpyramidal cell. Neuron 37, 977–987.CrossRefGoogle Scholar
  13. Poirazi, P., Brannon, T., and Mel, B. (2003b) Pyramidal neuron as two-layer neural network. Neuron 37, 989–999.CrossRefGoogle Scholar
  14. Press, W., Flannery, B., Teukolsky, S., and Vetterling, W. (1992) Numerial Recipes in C: The Artof Scientific Programming, 2nd ed., Cambridge University Press, Cambridge.Google Scholar

Copyright information

© Humana Press Inc 2006

Authors and Affiliations

  1. 1.Departments of Physiology, Pharmacology, and NeurologySUNY Downstate Medical CenterBrooklyn
  2. 2.Department of Electrical EngineeringPolytechnic UniversityBrooklyn

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