Title: SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series. Abstract: Continuous monitoring with an ever-increasing number of sensors has become ubiquitous across many application domains. Acquired data are typically high-dimensional and difficult to interpret, but they are also hypothesized to lie on a low-dimensional manifold. Dimensionality reduction techniques have, therefore, been sought for. Popular linear methods like Principle Component Analysis (PCA) have been extended to non-linear techniques such as Self-Organizing Maps (SOMs) or deep learning (DL) models. DL models have the ability to act on raw data, preventing heuristic feature selection, but the resulting latent space is often unstructured and still multi-dimensional. PCA and SOMs, on the other hand, need to be preceded with a feature-extraction step, but can then map high-dimensional features to 2D space. In this work we propose SOM-CPC, a model that jointly optimizes Contrastive Predictive Coding and a SOM to find an organized 2D manifold. We address a largely unexplored and challenging set of scenarios comprising high-rate time series, and show on both synthetic and real-life data (medical sleep data and audio recordings) that SOM-CPC outperforms both DL-based feature extraction, followed by PCA, K-means or a SOM, and strong deep-SOM baselines that jointly optimize a DL model and a SOM. SOM-CPC has great potential to expose latent patterns in high-rate data streams and may therefore contribute to a better understanding of many different processes and systems. 