Authors writing habits fluctuate throughout their lives. This evolution may stem from engaging in new topics, new genres or by the variation of their writing style. However, most representation models aiming at building meaningful authors embedding focus on static repre- sentations. They skip the precious time information useful to build more powerful and versatile representations. Only a limited number of meth- ods learn dynamic representations, each dedicated to a time bin. Here we propose a new representation learning model called BARL (Brown- ian Bridges for Author Representation Learning). BARL uses Brownian Bridges, a Gaussian process, to embed authors as continuous trajectories through time. Leveraging the Variational Information Bottleneck (VIB) framework, it integrates a pre-trained temporal text encoder to encode authors and documents into the same space, learning a distinct dynamic for each author along with a customized variance. We evaluate BARL on several tasks: authorship attribution, document dating and author classification on two datasets from the literature. BARL outperforms baselines and existing dynamic author embedding models while learning a continuous temporal representation space.