The feedback process that extends locally constant functions to larger domains is not limited to
supervised learning. The ‚SET‘ – input of the engine may be completely absent.
There are several ways to accomplish this:
This border case corresponds to a single neuron with, say, 65536 modifiable synapses organized into 256 dendritic compartments that computes a boolean function.
The plot below shows what happens with this feedback rule.
The pattern engine is exposed to millions of letter shapes in sequence. Each one creates a majority decision, and some fiber sizes change. Memory is initialized at random, the fibers all start at size 44000. After a short transient, the engine settles into interesting oscillation patterns. Each step on the ordinal axis corresponds to an entire page with some thousand letters.
Other values of γ give different pictures. Some combinations of γ , #X , #Y and , of course, input data seem to suppress the oscillations. There is certainly much room for further investigation.