When modelling networks of neurons, generally it is not possible to represent each neuron of the real system in the model. It is therefore essential to carry out appropriate simplifications for which many design questions have to be asked. These concern how each neuron should be modelled, the number of neurons in the model network and how the neurons should interact. To illustrate how these questions are addressed, networks using various types of model neuron are described. In some cases, the properties of each model neuron are represented directly in the model, and in others the averaged properties of a population of neurons. We then look at several large-scale models intended to model specific brain areas. In some of these models, the neurons are based on the neurons reconstructed from extensive anatomical and physiological measurements. The advantages and disadvantages of these different types of models are discussed.
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