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.
@article{arxiv.1606.01672,
title = {Predictive Coding for Dynamic Vision : Development of Functional Hierarchy in a Multiple Spatio-Temporal Scales RNN Model},
author = {Minkyu Choi and Jun Tani},
journal= {arXiv preprint arXiv:1606.01672},
year = {2017}
}