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The current paper presents a novel recurrent neural network model, thepredictive multiple spatio-temporal scales RNN (P-MSTRNN), which can generateas well as recognize dynamic visual patterns in the predictive codingframework. The model is characterized by multiple spatio-temporal scalesimposed on neural unit dynamics through which an adequate spatio-temporalhierarchy develops via learning from exemplars. The model was evaluated byconducting an experiment of learning a set of whole body human movementpatterns which was generated by following a hierarchically defined movementsyntax. The analysis of the trained model clarifies what types ofspatio-temporal hierarchy develop in dynamic neural activity as well as howrobust generation and recognition of movement patterns can be achieved by usingthe error minimization principle.

Predictive Coding for Dynamic Vision : Development of Functional Hierarchy in a Multiple Spatio-Temporal Scales RNN Model