Dynamical learning of neural networks based on chaotic dynamics
Kazuhiro Kojima, Kenichiro Ito · 2002
This paper proposes a new dynamical memory system based on chaotic neural networks, and its learning scheme. It is demonstrated that, when no embedded pattern, i.e., unknown pattern, is applied to the system, the output pattern travels around the embedded patterns, and the traveling phases depend on a external parameter of the networks such as the input from the other neurons or cortex. Further, the temporal output of the networks reflects a hierarchical structure of the memorized patterns.