The notions of feedback and feedforward information processing gained prominence under cybernetics, an early movement at the dawn of computer science and theoretical neuroscience. Negative feedback processing corrects errors, whereas feedforward processing makes predictions, thereby preemptively reducing errors. A key insight of cybernetics was that such processes can be applied to both perception, or state estimation, and control, or action selection. The remnants of this insight are found in many modern areas, including predictive coding in neuroscience and deep latent variable models in machine learning. This thesis draws on feedback and feedforward ideas developed within predictive coding, adapting them to improve machine learning techniques for perception (Part II) and control (Part III). Upon establishing these conceptual connections, in Part IV, we traverse this bridge, from machine learning back to neuroscience, arriving at new perspectives on the correspondences between these fields.</p
