Solving Bidirectional Tasks using MTRNN

In this paper we study the learning of bidirectional tasks in a Recurrent Neural Network (RNN). Most of such models deal with a flow of information in only one direction, either generating outputs or encoding inputs; However, using a single network to do both tasks simultaneously would be more efficient and biologically plausible. We will be using a Multiple Timescales Recurrent Neural Network (MTRNN) to solve these tasks. The network proves capable of dealing with this bidirectional-flow of information simply by training in both directions, with outputs becoming inputs and vice-versa. We showcase this behaviour on two tasks, using the same network. the first is a sentence learning task, akin to a classification problem. The second task is a motor trajectory learning task, akin to a regression problem. The data used in these tasks has been generated through an iCub robot. We present the results of these experiments and show that this model maintains its properties for the bidirectional tasks. We discuss possible future implementations using this ability to solve more complex scenarios such as action and language grounding.