In addition to the previously described data analysis and simulation routines, six tutorials illustrating their use are included with the Graphical Interface and Simulation Routines. The model illustrated in the figure above is part of one of these tutorials. We give here a short description of the tutorials, detailed instructions are included with each one of them.

**Tutorial 1.** This tutorial explains how to compute the ISI distribution, the power spectrum and the autocorrelation function of a neuronal spike train using the M-files described in sect. 3.

**Tutorial 2.** Here, the F-I curve of a leaky integrate-and-fire neuron is computed by simulation and compared to the theoretical F-I curve. This tutorial shows how to perform automatically several simulations with a model in which a parameter is varied from one simulation to the next (in this case the constant current injected in the neuron model).

**Tutorial 3.** A time-varying random stimulus is used as input to a Poisson spike generator and is then estimated from the spike train.

**Tutorial 4.** The first order transfer function of a model mimicking the processing of an LGN relay cell is computed by injecting a white random stimulus in the neuron and computing the cross-correlation between the input stimulus and the output spike train (see sect. 6.3 of chapter 9 in “Methods in Neuronal Modeling” for complete references).

**Tutorial 5.** This tutorial investigates the effect of refractoriness and multiplicative effects on the spiking output of a retinal ganglion cell model. Complete references and a short description of this model are given in sects. 3 and 4 of chapter 9 in “Methods in Neuronal Modeling”.

**Tutorial 6.** Here, the estimation of a time-varying stimulus from the spike trains of two half-wave rectifying neurons is explained using simple Poisson spike train generators. This technique was used experimentally on spike trains of a wide-field visual neuron of the fly and has been investigated theoretically, see sect. 6 of chapter 9 in “Methods in Neuronal Modeling” for details and a full list of references.

The spike trains and stimuli data sets resulting from simulations of the models introduced in the tutorials can also be downloaded, thus avoiding to actually go through the simulations themselves before performing the data analysis procedure described in the tutorials.