Time-Sequential Self-Organization of Hierarchical Neural Networks

Self-organization of multi-layered networks can be realized by time-sequential organization of successive neural layers. Lateral inhibition operating in the surround of firing cells in each layer provides for unsupervised capture of excitation patterns presented by the previous layer. By presenting patterns of increasing complexity, in coordination with network self-organization, higher levels of the hierarchy capture concepts implicit in the pattern set.