  The author previously presented an event window segmentation (EWS) algorithm
[5] that uses purely statistical methods to learn to recognize recurring
patterns in an input stream of events. In the following discussion, the EWS
algorithm is first extended to make predictions about future events. Next, this
extended algorithm is used to construct a high-level, simplified model of a
neocortical hierarchy. An event stream enters at the bottom of the hierarchy,
and drives processing activity upward in the hierarchy. Successively higher
regions in the hierarchy learn to recognize successively deeper levels of
patterns in these events as they propagate from the bottom of the hierarchy.
The lower levels in the hierarchy use the predictions from the levels above to
strengthen their own predictions. A C++ source code listing of the model
implementation and test program is included as an appendix.

Neural and Evolutionary Computing