Mathematical model for the self-organization of neural networks.
Mutual inhibition between neurons combined with a learning principle similar to that proposed by Hebb is shown to secure a powerful self-organizing property for neural networks. Numerical analysis reveals that the system investigated always organizes itself into the same final state from any arbitrarily chosen initial state.
D000200:Action Potentials; D001369:Axons / Q000648:ultrastructure; D002531:Cerebellum / Q000033:anatomy & histology; D006801:Humans; D007395:Interneurons / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net / Q000033:anatomy & histology* / Q000502:physiology; D009420:Nervous System / Q000033:anatomy & histology*; D009433:Neural Inhibition; D009474:Neurons / Q000502:physiology; D013569:Synapses / Q000502:physiology / Q000648:ultrastructure