Dynamic memorization characteristics in neural networks with different neuronal dynamics.
We introduce a stimulus-response scheme that supports plastic variation of synapse weights in neural networks, and analyze how memory formation evolves under external stimulation. In so doing, chaotic networks and stochastic networks that have very different dynamics are compared. Experimental results suggest that chaotic activity remarkably outperforms stochastic activity in stimulus-response memorization. This seems to be indicative of effectiveness of the chaos in dynamic learning by stimulus-response scheme oriented to natural learning.
Katada
Naofumi
N
Graduate School of Applied Informatics, University of Hyogo, Chuo-ku, Kobe, Hyogo 650-0044, Japan. nkatada@hyogo-c.ed.jp
Nishimura
Haruhiko
H
D003198:Computer Simulation; D007858:Learning / Q000502:physiology*; D008433:Mathematics; D008568:Memory / Q000502:physiology*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity; D009474:Neurons / Q000502:physiology*; D013269:Stochastic Processes