Control of associative dynamics by matching features in chaotic neural network
Toshinori Deguchi, Naohiro Ishii · Systems and Computers in Japan · 1996
Abstract When patterns are stored by using associative learning in a chaotic neural network consisting of chaotic neurons that easily produces chaos, whether dynamic (chaotic) or static (non‐chaotic) remembering occurs can be controlled by varying the parameters of the chaotic neurons. A pattern can be searched by using this dynamic remembering as a sampling procedure. If features of the output pattern of the network become similar to desired features during the search, the chaotic state is changed to a static remembering state so that the output pattern has desired features. In this paper, the state is controlled by using a model of presynaptic inhibition similar to that seen in real nerve cells (not using the parameters of the chaotic neurons). Extraction and comparison of the features are performed by back‐propagation networks. The extraction of features under this method is amenable to visual representation of its trajectory in feature space.