Predictive Motor Control Based on a Generative Adversarial Network

Abstract

Predictive processing models suggest that a brain decides actions through inference using its internal generative model over the worlds states and their transitions. Most of the predictive processing models have been formalized using explicit representations of the probability distributions, with explicit structures and parameters. They are difficult to learn in general and needs explanation about representation of structure and parameters of distributions, and the method for statistical arithmetic on them, each of which are questions not easy to answer. In this study, we explore an alternative representation for predictive processing which is based on an implicit model known as generative adversarial networks, which has been widely explored recently in machine learning studies as they can learn a distribution directly from data. We demonstrate how a generative adversarial network can be trained to learn an implicit generative model of motor dynamics. And then, we show that such a model can perform approximate inference using the trained model, providing the necessary computations for both the forward and inverse model of motor control. Our framework may provide another formalization for brains inference model, especially for learning process. Additionally, we suggest that the functional architecture of the cortical-basal ganglia circuit may modeled as the generator and discriminator in the generative adversarial network model.