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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