Control of multi-stable chaotic neural networks using input constraints

K Sets are nonlinear recurrent connectionist models proposed to emulate the brain dynamics. They can be used as dynamic memories encoding in non-equilibrium attractors. As multidimensional non-linear systems, they are extremely hard to analyze. Their dynamics is strongly believed to be related to the itinerant chaos introduced by Tsuda. In this contribution we design a system with attractor switching based on the previously obtained results. This is a step towards better understanding of the K models and building powerful chaotic neural memory systems.