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An advanced method for parallel computation of Lyapunov exponents in nonlinear parameter dependent continuous dynamical systems is presented. For computing exponents MATLAB with Parallel Toolbox was used. A simplest physical chaotic flow published in [2] was chosen and mathematically analysed. Obtained equation's parameters were calculated and synthesized into a circuit. Realization was implemented into a analog circuit and compared with the theory.

In this article a simple model of inertia neuron based on the ordinary differential equations of Hindmarsh-Rose model is shown. The fully analog circuit realization is presented. Operational amplifiers as an inverting integrator are used for integration of the differential equations. The function of the model is checked by varying of five bifurcation parameters. Simulations and measurements results are compared. The simulation and measured results demonstrate that analog neuron circuit model is the viable candidate for the implementation in real time.

This paper presents a novel analog oscillator reported recently by the first two authors. It came from the mathematical description of the so-called labyrinth chaos but it is much more suitable for fully analog circuitry implementation. The necessity of realization of the three independent channels performing goniometrical transfer functions is replaced by its rough approximation, namely the periodical sign functions. The brief mathematical background is completed by a number of the laboratory experiments. Finally some future perspectives, possible modifications and practical usability are discussed.