Before previously delineated investigation, I need to better understand fine-details of soft-clustering and variations most suited to neural flocking.  In particular, should an example be flocking to the soft-cluster's center or to the example's virtual location (based on a weighted combination of the clusters).  Should examples with netInputs peripheral to it's major cluster's center belong "totally" to that cluster?  i.e., a membership weight of 1.0 for that cluster and 0.0 for the other(s).  Would it be wise to switch-over to a pseudo-hard clustering once the memberships get highly skewed?   Momentum learning?  And, of course, the distance metrics related to the io functions "focussing of the clusters". -- and, of course, the ratio of derivatives as to it's relationship to generalization through gradient descent.
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