Some Background

Self-adaptive topological maps were initially inspired by modelling perception systems found in mammalian brain. A perception system involves the reception of external signals and their processing inside the nervous system. The complex mammalian skills, such as seeing and hearing seemed to bear similarity to each other in the way they worked. Namely, the primary characteristic of these systems is that neighbouring neurons encode input signals which are similar to each other.

General idea of the SOM model

The Self-Organizing Map (SOM) was introduced by Teuvo Kohonen in 1982. The SOM (also known as the Kohonen feature map) algorithm is one of the best known artificial neural network algorithms. In contrast to many other neural networks using supervised learning, the SOM is based on unsupervised learning.

The SOM is quite a unique kind of neural network in the sense that it constructs a topology preserving mapping from the high-dimensional space onto map units in such a way that relative distances between data points are preserved. The map units, or neurons, usually form a two-dimensional regular lattice where the location of a map unit carries semantic information. The SOM can thus serve as a clustering tool of high-dimensional data. Because of its typical two-dimensional shape, it is also easy to visualize.

Another important feature of the SOM is its capability to generalize. In other words, it can interpolate between previously encountered inputs.