Documentation ¶ Overview ¶ ra25 runs a simple random-associator four-layer leabra network that uses the standard supervised learning paradigm to learn mappings between 25 random input / output patterns defined over 5x5 input / output layers (i.e., 25 units) Source Files ¶ View all Source files ra25.go Click to show internal directories. Click to hide internal directories.