Adaptive Feedback Control of Chaotic Neurodynamics in Analog Circuits

We propose a control strategy of chaotic dynamics to stabilize periodic orbits in nonlinear discrete-time dynamical systems (maps) and apply it to a chaotic neuron map model not only numerically but also experimentally by analog circuit implementation. The control method is based on an adaptive feedback adjustment of a control parameter of the system, which uses typical bifurcation structures of nonlinear dynamical systems. We can observe a clear fractal structure in the sets composed of controlled states with respect to different initial conditions. We also discuss possible applications of the controlled system as an analog-valued memory with high-capacity.