Figure 5.
The abundance of correct modulatory weights resulting from stage-two training depends sensitively on DSC unit threshold θz and on modality-specific target probability ps. In A-C, the spontaneous primary input activation probability px0 equals 0.1. The primary input becomes less ambiguous as driven activation probability px1 is increased from 0.3 (A) to 0.6 (B) to 0.9 (C) (Table 3). The primary input threshold θx is increased from 4 (A) to 6 (B) to 10 (C). In A-C, the modulatory input spontaneous and driven activation probabilities py0 and py1 are 0 and 0.1, respectively, and the modulatory input threshold θy is 0. Modality-specific target probability ps is varied from 0 to 0.5 in steps of 0.025. Ten networks receive stage-one training for 5000 iterations at each ps value. Primary weights are pruned at θu = 0.4. DSC unit activity threshold θz is varied from 0 to 1 in steps of 0.05. Each of the 10 stage-one trained networks, at each ps value, receives stage-two training for 5000 iterations at each θz value. This yields 10 trained networks for each combination of ps and θz. Sets of 10 containing any misdirected modulatory weights (i.e., modulatory weights not respecting the modality-matching and cross-modality constraints) are excluded. For sets of 10 containing no such errors, the mean number of DSC units receiving modulatory connections is computed. Each panel plots the number of units, in error-free networks, that receive modulatory input. For px1 = 0.3 (A) stage-two works best when 0.2 ≤θz ≤ 0.3 and ps ≥ 0.15, for px1 = 0.6 (B) when 0.2 ≤θz ≤ 0.55 and ps ≥ 0.23, and for px1 = 0.9 (C) when 0.2 ≤θz ≤ 0.8 and ps ≥ 0.23. The number of error-free networks is greater for unambiguous than for ambiguous primary inputs.