Unsupervised learning of perceptual feature combinations
Fig 5
Histograms of neuron inputs (first column) and outputs v for the ALL-rule.
A: Equal presentation frequency; B: Different presentation frequency. Parameters: va = 0.7, ρ = 0.1, std = 0.1. Mean amplitudes of the inputs are indicated in the first column. Initial weights are ω(0) = [0.001, 0.001]T and initial learning rate is μ0 = 0.0005; Euler integration with step dt = 1. For other parameters: see plots. Response histograms (blue or yellow) in case of amplitude or presentation frequency difference are grouping very close to zero, where we truncate the zero bin to optimize for visibility (see truncation marks).