Unsupervised learning of perceptual feature combinations
Fig 13
Box plot for different combinations of inputs for the cases N = 5 inputs for the ALL-rule with 20% inhibitory cells.
Combinations are aligned in ascending order of active inputs, with color code indicating the number of inputs, see legend at the bottom. Combinations are indicated by decimal numbers corresponding to binary set notation (e.g. “3” means the combination: 00011, where only the two last inputs are active). “o” means other, where this denotes occurrences of cells signaling several different combinations. The size of the neural network is M = 200 (excitatory cells) with 40 inhibitory cells added. Average connectivity of excitatory cells onto excitatory cells is c = 2; connectivity onto inhibitory cells c = 20. Each excitatory cell, in addition, is given 10 inhibitory connections, with a fixed weights of 0.01. Annealing parameters are: annealing rate ρ = 0.3, where the annealing threshold va for each neuron individually is drawn from a uniform distribution [0.75,0.95]. Decision threshold is 0.7. Initial weights for excitatory inputs are chosen from Gaussian distribution with mean = 0.001 and std = 0.0002. Initial learning rate μ(0) = 0.0005. Euler integration with dt = 1. Median, mean and standard deviation are shown on the basis of 100 trials.