Geometry of Dynamic Movement Primitives in Neural Space: A FORCE-Learning Approach

Abstract

Dynamic movement primitives are one of key concepts for understanding dexterous and flexible movements of biological bodies. In the field of robotics engineering, simple types of nonlinear differential equations are used to generate movement primitives from demonstrations, but it remains unclear how nonlinear dynamics in the real brain can also generate movement primitives in biologically natural ways. The aim of this study is to investigate a possible role of nonlinear dynamics in random recurrent neural networks (RNNs) for skillful motor learning. We show that one-shot temporal patterns such arm reaching movements can be trained by a type of RNN-learning so-called FORCE-learning recently proposed by Sussillo and Abbott and a number of patterns are summarized as a manifold embedded in a space of synaptic weights of readout neurons. We also discuss how generalization of learning against untrained motor patterns can be achieved by identifying nonlinear coordinates (meta-parameters) on this manifold in a higher level of the central nervous system.