A self-organizing neural network sharing features of the mammalian visual system.

This paper describes a neural network model whose structure is designed to closely fit neuroanatomical and -physiological data, and not to be most suitable for rigorous mathematical analysis. It is shown by computer simulation that a process of self-organization that departs from a fixed retinotopic order at peripheral layers and includes Hebbian modifications of synaptic connectivity at higher processing levels leads to a system that is capable of mimicking various functions of visual systems: In the initial state the overall structure of the network is preset, individual connections at higher levels are randomly selected and their strength is initialized with random numbers. For this model the outcome of the self-organization process is determined by the stimulation during the developmental phase. Depending on the type of stimuli used the model can either develop towards a feature-selective "preprocessor" stage in a complex vision system or towards a subsystem for associative recall of abstract patterns. This flexibility supports the hypothesis that the principles embodied are rather universal and can account for the development of various nervous system structures.