A new type of self-organization associated with chaotic dynamics in neural networks.
A new type of self-organized dynamics is presented, in relation with chaos in neural networks. One is chaotic itinerancy and the other is chaos-driven contraction dynamics. The former is addressed as a universal behavior in high-dimensional dynamical systems. In particular, it can be viewed as one possible form of memory dynamics in brain. The latter gives rise to singular-continuous nowhere-differentiable attractors. These dynamics can be related to each other in the context of dimensionality and of chaotic information processings. Possible roles of these complex dynamics in brain are also discussed.
Tsuda
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Department of Mathematics, Graduate School of Science, Hokkaido University, Sappro, Japan.
D000818:Animals; D019277:Entropy; D005246:Feedback; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics; D009830:Olfactory Bulb / Q000502:physiology*