Autogenerative nodal memory model (ANM)-an analysis of growth metrics

The autogenerative nodal memory model (ANM) has been described in detail in the literature, and various experiments have been conducted to test the validity of the model. The authors describe how such a system evolves under certain input conditions. This analysis is part of the results obtained from the mathematical analysis that is currently being published. It gives insight on how the ANM system categorizes the events perceived, and most of all, on what are the related growth metrics. Two interesting results are obtained. First, as N grows, the number of nodes and interconnections grows much slower than the sequence length as N grows. This is essential for the ANM in the sense that it tends to saturate with difficulty. The second result is that actual sequences, with true probability distribution of events, will tend to generate networks which are bound by these maximum limit metric values, which are in a usable range