C++ Neural Networks and Fuzzy Logic: Preface

The Kohonen Network

The Kohonen network has two layers, an input layer and a Kohonen output layer. (See Figure 11.4). The input

layer is a size determined by the user and must match the size of each row (pattern) in the input data file.

Figure 11.4

  A Kohonen network.

Modeling Lateral Inhibition and Excitation

The mexican hat function shows positive values for an immediate neighborhood around the neuron and

negative values for distant neurons. A true method of modeling would incorporate mutual excitation or

support for neurons that are within the neighborhood (with this excitation increasing for nearer neurons) and

inhibition for distant neurons outside the neighborhood. For the sake of computational efficiency, we model

lateral inhibition and excitation by looking at the maximum output for the output neurons and making that

output belong to a winner neuron. Other outputs are inhibited by setting their outputs to zero. Training, or

weight update, is performed on all outputs that are within a neighborhood size distance from the winner

neuron. Neurons outside the neighborhood do not participate in training. The true way of modeling lateral

inhibition would be too expensive since the number of lateral connections is quite large. You will find that

this approximation will lead to a network with many if not all of the properties of a true modeling approach of

a Kohonen network.