  The current paper presents a novel recurrent neural network model, the
predictive multiple spatio-temporal scales RNN (P-MSTRNN), which can generate
as well as recognize dynamic visual patterns in the predictive coding
framework. The model is characterized by multiple spatio-temporal scales
imposed on neural unit dynamics through which an adequate spatio-temporal
hierarchy develops via learning from exemplars. The model was evaluated by
conducting an experiment of learning a set of whole body human movement
patterns which was generated by following a hierarchically defined movement
syntax. The analysis of the trained model clarifies what types of
spatio-temporal hierarchy develop in dynamic neural activity as well as how
robust generation and recognition of movement patterns can be achieved by using
the error minimization principle.
