This is a two paradigm process :
Associative Mapping : Here the network produces a pattern output by working in a pattern on the given input.
Regularity Detection : In this, units learn to respond to particular properties of the input patterns. Whereas in associative mapping the network stores the relationships among patterns, in regularity detection the response of each unit has a particular ‘meaning’. This type of learning mechanism is essential for feature discovery and knowledge representation.
