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After an elementary derivation of the "time transformation", mapping acounting process onto a homogeneous Poisson process with rate one, a briefreview of Ogata's goodness of fit tests is presented and a new test, the"Wiener process test", is proposed. This test is based on a straightforwardapplication of Donsker's Theorem to the intervals of time transformed countingprocesses. The finite sample properties of the test are studied by Monte Carlosimulations. Performances on simulated as well as on real data are presented.It is argued that due to its good finite sample properties, the new test isboth a simple and a useful complement to Ogata's tests. Warnings are moreovergiven against the use of a single goodness of fit test.

On Goodness of Fit Tests For Models of Neuronal Spike Trains Considered as Counting Processes