Electrical models of neurons are one of the rather rare cases in Biology where a concise quantitative theory accounts for a huge range of observations and works well to predict and understand physiological properties. The mark of a successful theory is that people take it for granted and use it casually. Single neuronal models are no longer remarkable: with the theory well in hand, most interesting questions using models have moved to the networks of neurons in which they are embedded, and the networks of signalling pathways that are in turn embedded in neurons. Nevertheless, good single-neuron models are still rather rare and valuable entities, and it is an important goal in neuroinformatics (and this chapter) to make their generation a well-tuned process.
The electrical properties of single neurons can be acurately modeled using multicompartmental modeling. Such models are biologically motivated and have a close correspondence with the underlying biophysical properties of neurons and their ion channels. These multicompartment models are also important as building blocks for detailed network models. Finally, the compartmental modeling framework is also well suited for embedding molecular signaling pathway models which are important for studying synaptic plasticity. This chapter introduces the theory and practice of multicompartmental modeling.
Neuronal Model Nernst Equation Passive Property Cable Equation Nernst Potential
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Cooley JW, Dodge FA (1966) Digital computer solutions for excitation and propagation of the nerve impulse. Biophys J 6(5):583–599PubMedCrossRefGoogle Scholar
Cornelis H, Rodriguez AL, Coop AD, Bower JM (2012) Python as a federation tool for GENESIS 3.0. PLoS One 7(1):e29018Google Scholar
Crook S, Gleeson P, Howell F, Svitak J, Silver RA (2007) MorphML: level 1 of the NeuroML standards for neuronal morphology data and model specification. Neuroinformatics 5(2): 96–104PubMedCrossRefGoogle Scholar
Eppler JM, Helias M, Muller E, Diesmann M, Gewaltig M (2008) PyNEST: a convenient interface to the NEST simulator. Front Neuroinformatics 2:12Google Scholar
Fitzhugh R (1961) Impulses and physiological states in theoretical models of nerve membrane. Biophys J 1(6):445–466PubMedCrossRefGoogle Scholar
Gillespie DT (1977) Exact stochastic simulation of coupled chemical reactions. J Phys Chem 25:2340–2361Google Scholar
Gleeson P, Steuber V, Silver RA (2007) NeuroConstruct: a tool for modeling networks of neurons in 3D space. Neuron 54(2):219–235PubMedCrossRefGoogle Scholar
Hille B (1992) Ionic channels of excitable membranes. Sinauer Associates Inc., SunderlandGoogle Scholar
Traub RD, Wong RK, Miles R, Michelson H (1991) A model of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. J Neurophysiol 66(2): 635–650PubMedGoogle Scholar
Van Geit W, Achard P, De Schutter E (2007) Neurofitter: a parameter tuning package for a wide range of electrophysiological neuron models. Front Neuroinformatics 1:1Google Scholar
Wils S, De Schutter E (2009) STEPS: modeling and simulating complex reaction–diffusion systems with Python. Front Neuroinformatics 3:15Google Scholar
Yamada W, Koch C, Adams PR (1989) Multiple channels and calcium dynamics. In: Koch S (ed) Methods in neuronal modeling. MIT Press, Cambridge, pp 97–133Google Scholar