Unsupervised Learning
In all the forms of learning we have met so far the answer that the network is supposed to give for the training examples is known. That type of learning requires a teacher who knows the correct classification for the input patterns in the training set. The objective is typically to generalise from these to other, previously unseen examples: giving more or less correct answers without intervention. In unsupervised learning the aim is rather different. The objective is, put most simply, to find the natural structure inherent in the input data. There are a number of unsupervised learning schemes, including competitive learning, adaptive resonance theory and Self-Organising Feature Maps (SOFMs). A well known type of SOFM is a Kohonen network.
http://www.let.rug.nl/~kleiweg/kohonen/kohonen.html
Kohonen maps are self-organizing neural networks that classify and quantify n-dimensional data into a one- or two-dimensional array of neurons [1]-[2]. Most applications of Kohonen maps use simulations on conventional computers, eventually coupled to hardware accelerators or dedicated neural computers. The small number of different operations involved in the combined learning and classification process makes however the Kohonen model particularly suited to a dedicated VLSI implementation, taking full advantage of the parallelism and speed that can be obtained on the chip. We propose here a fully analog implementation of a one-dimensional Kohonen map, with on-chip learning and refreshment of on-chip analog synaptic weights
