ch4/

err_driven_hidden shows how XCal error driven learning can train a hidden layer to solve problems that are otherwise impossible for a simple two layer network (as we saw in the Pattern Associator exploration, which should be completed first before doing this one).

family_trees shows how learning can recode inputs that have no similarity structure into a hidden layer that captures the *functional* similarity structure of the items.

hebberr_combo shows how XCal hebbian learning in shallower layers of a network can aid an error driven learning network to generalize to unseen combinations of patterns.

pat_assoc illustrates how error-driven and hebbian learning can operate within a simple task-driven learning context, with no hidden layers.

self_org illustrates how self-organizing learning emerges from the interactions between inhibitory competition, rich-get-richer Hebbian learning, and homeostasis (negative feedback).