One model of a laser is a set of differential equations called the Maxwell-Bloch equations. Actually, in a physical system, causing a chaotic behavior is very difficult. However the chaotic behavior can be observed easily in the system which has a mirror to feedback the delayed output.

Various models of a neuron have been proposed and many studies about them and their networks have been reported. Among these neurons, this paper describes a study about the model of a neuron providing its own feedback input and possesing a chaotic dynamics. Using a return map or a histogram of laminar length, type-I intermittency is recognized in a recurrent neuron and its network. A posibility of controlling dynamics in recurrent neural networks is also mentioned a little in this paper.

This letter describes one neuron's dynamics. This neuron provides its own feedback input. We call this neuron the recurrent neuron and investigate its nonlinear dynamics.

This letter discusses a behavior of solitons in a Josephson junction transmission line which is described by a perturbed sine-Gordon equation. It is shown that a soliton wave leads a quasi-periodic break down route to chaos in a Josephson transmission line. This route show phase locking, quasi-periodic state, chaos and hyper chaos, and these phenomena are examined by using Poincar sections, circle map, rotation number, and so on.

Recently, the back propagation method, which is one of the algorithms for learning neural networks, has been widely applied to various fields because of its excellent characteristics. But it has drawbacks, for example, slowness of learning speed, the possibility of falling into a local minimum and the necessity of adjusting a learning constant in every application. In this article we propose an algorithm which overcomes some of the drawbacks of the back propagation by using an improved genetic algorithm.