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KInNeSS: A Modular Framework for Computational Neuroscience

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Abstract

Making use of very detailed neurophysiological, anatomical, and behavioral data to build biologically-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalability, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multi-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions or ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further development of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effectively collaborate using a modern neural simulation platform.

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Notes

  1. http://www.neuroml.org

  2. KInNeSS is licensed under the GNU general public license; © Anatoli Gorchetchnikov.

  3. http://www.kinness.net

  4. http://www.kinness.net/Docs/SANNDRA/html/index.html

  5. SANNDRA is licensed under the GNU general public license; © Anatoli Gorchetchnikov.

  6. http://kinness.net/Docs/KInNeSS/examples/Example_Receptor_Kinness.rar

  7. http://www.kinness.net/Docs/KInNeSS/manual/index.html

  8. http://www.kinness.net/kinness_tutorial.wmv

  9. http://www.w3.org/TR/xslt.html

  10. The equations in “XML Support” section follow sign conventions found in the literature. Note that in the KInNeSS interface, this convention is reversed.

  11. When loading spatial input patterns from a .png file, the yellow channel represents the alpha channel.

  12. The membrane potential outputted by KInNeSS is shifted by an amount equal to the leak potential of the compartment. For example, when the user sets the compartmental leakage reverse potential to -60 mV, a value of zero in the output file corresponds to a membrane potential of -60 mV.

  13. http://www.kde.org

  14. KDE always list native KDE classes with K in the front. All names that start with K and are followed by a capital letter are inherited from KDE native classes.

  15. http://kinness.net/

  16. See either ModelDB (https://senselab.med.yale.edu/modeldb/ShowModel.asp?model=113939) or the documentation section of http://www.kinness.net for the code and data.

  17. See either ModelDB (https://senselab.med.yale.edu/modeldb/ShowModel.asp?model=113939) or the documentation section of http://www.kinness.net for the code and data.

  18. https://computation.llnl.gov/casc/sundials/main.html

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Acknowledgements

This work was supported by the Center of Excellence for Learning in Education, Science and Technology (NSF SBE-0354378). Massimiliano Versace was supported in part by the Air Force Office of Scientific Research (AFOSR F49620-01-1-0397), the National Science Foundation (NSF SBE-0354378), and the Office of Naval Research (ONR N00014-01-1-0624). Heather Ames, and Jasmin Léveillé were supported in part by the National Science Foundation (NSF SBE-0354378) and the Office of Naval Research (ONR N00014-01-1-0624). Bret Fortenberry and Anatoli Gorchetchnikov were supported in part by the National Science Foundation (NSF SBE-0354378). The authors would also like to thank Prof. Steve Grossberg, Himanshu Mhatre, Prof. Mike Hasselmo, Dash Sai Gaddam, and Jesse Palma for numerous valuable discussions and suggestions for this paper.

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  1. Department of Cognitive and Neural Systems, Boston University, 677 Beacon Street, Boston, MA, 02215, USA

    Massimiliano Versace, Heather Ames, Jasmin Léveillé, Bret Fortenberry & Anatoli Gorchetchnikov

  2. Center of Excellence for Learning In Education, Science, and Technology, Boston University, Boston, MA, USA

    Massimiliano Versace, Heather Ames, Jasmin Léveillé, Bret Fortenberry & Anatoli Gorchetchnikov

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  1. Massimiliano Versace
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Correspondence to Massimiliano Versace.

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Versace, M., Ames, H., Léveillé, J. et al. KInNeSS: A Modular Framework for Computational Neuroscience. Neuroinform 6, 291–309 (2008). https://doi.org/10.1007/s12021-008-9021-2

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