Before revision: Trained recurrent neural networksare commonly used both in neuroscience and in machine learning. Recently, several attempts were made to understand the dynamics of such trained networks. Understanding how these dynamics arise through learning , however, is an uncharted territory. Here, we study this process in a simple setting -- learning to memorize analog values, leading to the formation of a line attractor  .
After revision: Artificial neural networks, trained to perform cognitive tasks, have recently been used as models for neural recordings from animals performing these tasks. While some progress has been made in performing such comparisons, the evolution of network dynamics throughout learning remains unexplored. This is paralleled by an experimental focus on recording from trained animals, with few studies following neural activity throughout training. In this work, we address this gap in the realm of artificial networks by analyzing networks that are trained to perform memory and pattern generation tasks. The functional aspect of these tasks corresponds to dynamical objects in the fully trained network - a line attractor .