  Studies suggest that within the hierarchical architecture, the topological
higher level possibly represents a conscious category of the current sensory
events with slower changing activities. They attempt to predict the activities
on the lower level by relaying the predicted information. On the other hand,
the incoming sensory information corrects such prediction of the events on the
higher level by the novel or surprising signal. We propose a predictive
hierarchical artificial neural network model that examines this hypothesis on
neurorobotic platforms, based on the AFA-PredNet model. In this neural network
model, there are different temporal scales of predictions exist on different
levels of the hierarchical predictive coding, which are defined in the temporal
parameters in the neurons. Also, both the fast and the slow-changing neural
activities are modulated by the active motor activities. A neurorobotic
experiment based on the architecture was also conducted based on the data
collected from the VRep simulator.
