In this paper, two generalizations of the SOM are introduced. The first of these extends the SOM to deal with more generalized classes of objects besides the vector dataset. This generalization is realized by employing modular networks instead of reference vector units and is thus called a modular network SOM (mnSOM). The second generalization involves the extension of the SOM from "map" to "homotopy", allowing the SOM to deal with a set of data distributions rather than a set of data vectors. The resulting architecture is called SOM^n, where each reference unit represents a tensor of rank n. These generalizations are expected to provide good platforms on which to build brain-like intelligence.
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