Robust oscillations and bifurcations in cellular neural networks

A software package for studying discrete time neural networks dynamics is presented as an efficient analysis tool having also capabilities for designing special purpose cellular neural networks (CNN´s) such as period controlled oscillators, noise generators and chaotic based systems. Based on an information theory approach, an entropy associated with a given structure of the network was defined as a global descriptor of the system dynamics. Any kind of discrete time neural model with any size is allowed, including CNN´s as a particular case. By performing two or one dimensional analysis trough the weights space, some new properties of the opposite-template CNN´s were discovered, e.g. the existence of robustness domains which means that small controllable change in weights values implies no change in the network´s entropy. Using this software package, robust chaotic networks capable of generating white-noise like signals were also discovered