Neuroinformatics

, Volume 6, Issue 4, pp 291–309 | Cite as

KInNeSS: A Modular Framework for Computational Neuroscience

  • Massimiliano Versace
  • Heather Ames
  • Jasmin Léveillé
  • Bret Fortenberry
  • Anatoli Gorchetchnikov
Article

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.

Keywords

Behavioral modeling Compartmental modeling Object oriented design Spiking neurons Software framework 

Notes

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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Copyright information

© Humana Press 2008

Authors and Affiliations

  • Massimiliano Versace
    • 1
    • 2
  • Heather Ames
    • 1
    • 2
  • Jasmin Léveillé
    • 1
    • 2
  • Bret Fortenberry
    • 1
    • 2
  • Anatoli Gorchetchnikov
    • 1
    • 2
  1. 1.Department of Cognitive and Neural SystemsBoston UniversityBostonUSA
  2. 2.Center of Excellence for Learning In Education, Science, and TechnologyBoston UniversityBostonUSA

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