Analyzing learning dynamics: How to average?

Pattern-based learning processes are usually analyzed by means of probability density functions of the weights or moments thereof. During the derivation of these equations, some averaging has to be performed. We show that the manner of averaging is crucial for the results of the analysis. We do this by comparing two types of analysis (Langevin type and discrete-time moments) for one learning system.