Unsupervised learning of perceptual feature combinations
Fig 8
Three input coincidence sorting for ALL and BCM rules.
A: Output histograms. Note that 3 examples for BCM are shown using the same intrinsic parameters but different stimulus sequencing. B: Weight development. Note the different x-axis scales. C: Parameter space analysis: Errors for classification “one active input”, “two active inputs”, “three active inputs” are based on response thresholds 0.25 and 0.75, averages over 20 trials are shown. Light color corresponds to good coincidence sorting. Circles in the error plots show parameter combinations for which histograms are shown in panel (A). Parameters: mean amplitude is 1 in case the input is active, STD = 0.1, ω(0) = [0.2, 0.2, 0.2]T, μ = 0.001, pair-coincidence 30% for every possible combination (12, 13, 23) in respect to that pair, triple co-incidence for 123: 6%; for BCM: ΘM(0) = 0.1; Euler integration with dt = 1 in all cases.