Fig. 6. Hebbian learning network. (A) Architecture of network. Each configural unit
receives weighted input from all contextual (C1, C2...Cn) and local
(L1, L2...Ln) units through plastic links that are subject to Hebbian
and anti-Hebbian reinforcement during training. Inhibitory connections between
the configural units produce a `winner-takes-all' output. The most active unit
inhibits the rest and excites the output node (O). (B) Training cycle in
pseudo code. Learning rules in the body of the code are applied until the
network responds correctly or the permissible number of training cycles
(MAX_TRAIN_CYCLES) is exceeded. (C) Flow chart of comparison of sequential and
simultaneous training, as outlined in the text. av., average; std., standard
deviation.
