Neural networks and conditional association networks: common properties and differences

At first it is shown how large networks can be represented in an illustrative way. Then, the special aspects of neural networks are discussed. When conditional association arrays and semantic memories are plotted as networks surprising similarities arise, but important characteristic differences can be seen too. The first experiments suggest that substantial progress in text processing may be achieved faster with conditional association networks than with neural networks.