Fig. 2From: Multiscale analysis of slow-fast neuronal learning models with noiseThe first two figures, (a) and (b), represent the evolution of the connectivity for original stochastic system (12), superimposed with averaged system (13), for two different values of ϵ: respectively ϵ=0.01 and ϵ=0.001, where we have chosen ϵ= ϵ 1 = ϵ 2 . Each color corresponds to the weight of an edge in a network made of n=3 neurons. As expected, it seems that the smaller ϵ, the better the approximation. This can be seen in the picture (c) where we have plotted the precision on the y-axis and ϵ on the x-axis. The parameters used here are l=12, μ=1, κ=100, σ=0.05. The inputs have a random (but frozen) spatial structure and evolve according to a sinusoidal function.Back to article page