  This paper introduces the concept of a bi-scale metric for use in the
cooperative phase of the self-organizing map (SOM) algorithm. Use of a bi-scale
metric allows segmentation of the map into a number of regions, corresponding
to anticipated cluster structure in the data. Such a situation occurs, for
example, in the somatotopic maps which inspired the SOM algo- rithm, where
clusters of data may correspond to body surface regions whose general structure
is known. When a bi-scale metric is appropriately applied, issues with map
neurons that are not activated by any point in the training data are reduced or
eliminated. The paper also presents results of simulation studies on the
plasticity of bi-scale metric maps when they are retrained af- ter loss of
groups of map neurons or after changes in training data (such as would occur in
a somatotopic map when a body surface region like a finger is lost/removed).
The paper further considers situations where tri-scale met- rics may be useful,
and an alternative approach suggested by neurobiology, where some map regions
adapt more slowly to stimuli because they have a lower learning rate parameter.
