  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.

Computer Vision and Pattern Recognition