Weighting features from above and below

I have experimented with weighting input features in Asa H.
Forward/upward weighting:  When a category is active in a layer of the Asa H hierarchy a utility value for that category can be passed up to the next Asa H layer along with that category's current  activity value.  This is one of the input features for the next layer in the hierarchy.  That feature's input activation can be weighted with its accompanying utility value.
Backward/downward weighting: As input features are compared with and activate a category in a given layer this (output) category has, itself, a utility which can be used as a weight for the input features. (Trans. Kan. Acad. Sci., vol 109, no 3/4, pg 160, 2006)
Some other weightings:
Weight a feature according to how often it is seen.
Weight a feature according to how often it changes.
Weight a feature according to some average of the utilities of the categories it occurs in.