  As a phenomenon in dynamical systems allowing autonomous switching between
stable behaviors, chaotic itinerancy has gained interest in neurorobotics
research. In this study, we draw a connection between this phenomenon and the
predictive coding theory by showing how a recurrent neural network implementing
predictive coding can generate neural trajectories similar to chaotic
itinerancy in the presence of input noise. We propose two scenarios generating
random and past-independent attractor switching trajectories using our model.
