The main question addressed by modeling is whether what we know about a system can account for the behavior of that system. Complex models provide synthesis of a large body of experimental data. A well designed model not only demonstrates that our hypothesis is compatible (or not) with the real system, but also points to the missing data and suggests experiments which are necessary to fill the gap in our knowledge. This article gives a concise review of the simulation methods used in single cell level neuron models which are closely linked to experimental data. The membrane model and its electrical equivalent circuit and the modeling of active conductances using the Hodgkin-Huxley formalism are presented. The non-isopotential cylinder is presented very briefly and for the simulation of intricated neuronal morphologies the compartmental modeling is introduced.
Keywords: central nervous system, membrane model, Hodgkin-Huxley formalism, cable equation, compartmental modeling.