What is a Self Organizing Map?
So far we have looked at networks with supervised training techniques, in which there is a
target output for each input pattern, and the network learns to produce the required outputs.
We now turn to unsupervised training, in which the networks learn to form their own
classifications of the training data without external help. To do this we have to assume that
class membership is broadly defined by the input patterns sharing common features, and
that the network will be able to identify those features across the range of input patterns.
One particularly interesting class of unsupervised system is based on competitive learning,
in which the output neurons compete amongst themselves to be activated, with the result
that only one is activated at any one time. This activated neuron is called a winner-takesall
neuron or simply the winning neuron. Such competition can be induced/implemented
by having lateral inhibition connections (negative feedback paths) between the neurons.
The result is that the neurons are forced to organise themselves. For obvious reasons, such
a network is called a Self Organizing Map (SOM)