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.