SELF ORGANISING MAPS (Kohonen)

In the models so far a key role was played by the connections between neurons (Hopfield, Multi-layer feed forward).

However so far the location of each neuron within a layer has played no role for the outgoing or incoming connections.

With self organising maps one deals with models in which the ordlering or neurons i.e. within a layer structure plays an important role.

One is concerned with how neurons should organise their connectivity in order to optimise the spatial distribution of their responses within the layer .

The purpose of optimisation here is to convert similarity of signal into
proximity of excited neurons. Neurons with similar tasks can
communicate over short connection paths.

Important case is that of topographically organised projections from a receptor layer to a layer of sensory neurons.

We can observe the formation of topographic feature maps where the most important similarity relationships among the input signals are converted into spatial relationships among the corresponding neurons.

We might be dealing with a stimulus position on a sensory surface such as the retina or bodys outer surface or else more abstract features such as pitch or sound intensity .