Neocognitron capable of incremental learning.
This paper proposes a new neocognitron that accepts incremental learning, without giving a severe damage to old memories or reducing learning speed. The new neocognitron uses a competitive learning, and the learning of all stages of the hierarchical network progresses simultaneously. To increase the learning speed, conventional neocognitrons of recent versions sacrificed the ability of incremental learning, and used a technique of sequential construction of layers, by which the learning of a layer started after the learning of the preceding layers had completely finished. If the learning speed is simply set high for the conventional neocognitron, simultaneous construction of layers produces many garbage cells, which become always silent after having finished the learning. The proposed neocognitron with a new learning method can prevent the generation of such garbage cells even with a high learning speed, allowing incremental learning.
Fukushima
Kunihiko
K
School of Media Science, Tokyo University of Technology, 1404-1 Katakura Hachioji, Tokyo 192-0982, Japan. fukushima@media.teu.ac.jp
D003071:Cognition / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000145:classification / Q000502:physiology; D017729:Presynaptic Terminals / Q000502:physiology; D012054:Reinforcement, Psychology