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Simulator for Neural Networks and Action Potentials

Description and Applications

  • Protocol
Neuroinformatics

Part of the book series: Methods in Molecular Biology™ ((MIMB,volume 401))

Summary

A key challenge for neuroinformatics is to devise methods for representing, accessing, and integrating vast amounts of diverse and complex data. A useful approach to represent and integrate complex data sets is to develop mathematical models [Arbib (The Handbook of Brain Theory and Neural Networks, pp. 741–745, 2003); Arbib and Grethe (Computing the Brain: A Guide to Neuroinformatics, 2001); Ascoli (Computational Neuroanatomy: Principles and Methods, 2002); Bower and Bolouri (Computational Modeling of Genetic and Biochemical Networks, 2001); Hines et al. (J. Comput. Neurosci. 17, 7–11, 2004); Shepherd et al. (Trends Neurosci. 21, 460–468, 1998); Sivakumaran et al. (Bioinformatics 19, 408–415, 2003); Smolen et al. (Neuron 26, 567–580, 2000); Vadigepalli et al. (OMICS 7, 235–252, 2003)]. Models of neural systems provide quantitative and modifiable frameworks for representing data and analyzing neural function. These models can be developed and solved using neurosimulators. One such neurosimulator is simulator for neural networks and action potentials (SNNAP) [Ziv (J. Neurophysiol. 71, 294–308, 1994)]. SNNAP is a versatile and user-friendly tool for developing and simulating models of neurons and neural networks. SNNAP simulates many features of neuronal function, including ionic currents and their modulation by intracellular ions and/or second messengers, and synaptic transmission and synaptic plasticity. SNNAP is written in Java and runs on most computers. Moreover, SNNAP provides a graphical user interface (GUI) and does not require programming skills. This chapter describes several capabilities of SNNAP and illustrates methods for simulating neurons and neural networks. SNNAP is available at http://snnap.uth.tmc.edu.

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Acknowledgments

We thank Drs. E. Av-Ron and G. Phares for helpful comments on an earlier draft of this manuscript. This work was supported by NIH grants R01RR11626 and P01NS38310.

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Authors and Affiliations

  1. Department of Neurobiology and Anatomy, The University of Texas Medical School at Houston, Houston, TX

    Douglas A. Baxter & John H. Byrne

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  1. Douglas A. Baxter
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  2. John H. Byrne
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© 2007 Humana Press Inc.

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Baxter, D.A., Byrne, J.H. (2007). Simulator for Neural Networks and Action Potentials. In: Neuroinformatics. Methods in Molecular Biology™, vol 401. Humana Press. https://doi.org/10.1007/978-1-59745-520-6_8

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  • DOI: https://doi.org/10.1007/978-1-59745-520-6_8

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-58829-720-4

  • Online ISBN: 978-1-59745-520-6

  • eBook Packages: Springer Protocols

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