Self-Organizing Maps (SOM), or Kohonen maps as you may have heard, are one of the basic types of self-organizing neural networks. The ability to self-organize provides adaptation to formerly unseen input data. It has been theorized as one of the most natural ways of learning, like that which is used by our brains, where no predefined patterns are thought to exist. Those patterns take shape during the learning process and are incredibly gifted at representing multidimensional data at a much lower level of dimensionality, such as 2D or 1D. Additionally, this network stores information in such a way that any topological relationships within the training set remain preserved.