Simulator for neural networks and action potentials.
Simulator for neural networks and action potentials.
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DOI：
10.1007/978-1-59745-520-6_8
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发表时间：
2007-01-01
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影响因子：
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通讯作者：
Byrne, John H
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文献类型：
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作者：
Baxter, Douglas A;Byrne, John H
神经信息学面临的一个关键挑战是设计用于表示、访问和整合大量多样和复杂数据的方法。表示和整合复杂数据集的有用方法是开发数学模型[Arbib（The Handbook of Brain Theory and Neural Networks，pp. 741-745，2003）; Arbib和Grethe（Computing the Brain：A Guide to Neuroinformatics，2001）; Ascoli（Computational Neuroanatomy：Principles and Methods，2002）; Bower和Bolouri（Computational Modeling of Genetic and Biochemical Networks，2001）; Hines等（J. Comput. Neurosci. 17，7-11，2004）; Shepherd等人（Trends Neurosci. 21，460-468，1998）; Sivakumaran等人（Bioinformatics 19，408-415，2003）; Smolen等人（Neuron 26，567-580，2000）; Vadigepalli等人（OMICS 7，235-252，2003）]。神经系统模型为数据表示和神经功能分析提供了定量和可修改的框架。这些模型可以使用神经模拟器来开发和求解。一种这样的神经模拟器是神经网络和动作电位模拟器（SNNAP）[Ziv（J. Neurophysiol. 71，294-308，1994）]。SNNAP是一个多功能和用户友好的工具，用于开发和模拟神经元和神经网络模型。SNNAP模拟神经元功能的许多特征，包括离子电流及其通过细胞内离子和/或第二信使的调节，以及突触传递和突触可塑性。SNNAP是用Java编写的，可以在大多数计算机上运行。此外，SNNAP提供图形用户界面（GUI），不需要编程技能。本章介绍SNNAP的几种功能，并举例说明模拟神经元和神经网络的方法。SNNAP可在http://snnap.uth.tmc.edu上获得。
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 .