Unsupervised learning of perceptual feature combinations
Fig 9
Input coincidence sorting properties under more variable conditions.
A: Results for the ALL rule for the three input case with amplitude variation. Errors for classification “one active input”, “two active inputs”, “three active inputs” are based on thresholds 0.25 and 0.75. Light color corresponds to good coincidence sorting. Parameters: average amplitude provided above the plots, STD = 0.1, ω(0) = [0.2, 0.2, 0.2]T, μ(0) = 0.001, pair-coincidence 30% for every possible combination (12, 13, 23) in respect to that pair, triple co-incidence for 123: 6%; plots show averages over 20 trials. Histograms of individual runs below correspond to the two circles in parameter plots above. B: Results on input coincidence sorting for ALL and BCM (Intrator-Cooper) rule for a five input case. For 5 inputs there are 31 possible combinations of neurons driven by n ≥ 1 inputs: 5 × 1, 10 × 2, 10 × 3, 5 × 4 and 1 × 5 inputs as indicated beneath the abscissa. Parameters: ω(0) = [0.1, 0.1, 0.1, 0.1, 0.1]Tμ = 0.001, binary subsets of five presented in equal probability, random order, Euler integration with dt = 1 in both cases, for ALL: va = 0.7, ρ = 0.1, for BCM: ΘM(0) = 0.2, v0 = 0.4, γ = 10.