ABSTRACT
One of the most powerful aspects of neural networks is their ability to adapt to problems by changing their interconnection strengths according to a given learning rule. On the other hand, one of the main drawbacks of neural networks is the lack of knowledge for determining the topology of the network, that is, the number of layers and number of neurons per layer. A relatively new class of neural networks tries to overcome this problem by letting the network also automatically adapt its topology to the problem. These are the so-called ontogenic neural networks. This section provides an extensive survey and comparison of these methods.
