In recent years, there has been a growing appreciation for the importance of respecting the topological, algebraic, or geometric structure of data in machine learning models. In parallel, an emerging set of findings in computational neuroscience suggests that the preservation of this kind of mathematical structure may be a fundamental principle of neural coding in biology. The goal of this workshop is to bring together researchers from applied mathematics and deep learning with neuroscientists whose work reveals the elegant implementation of mathematical structure in biological neural circuitry. Group theory and differential geometry were instrumental in unifying the models of 20th-century physics. Likewise, they have the potential to unify our understanding of how neural systems form useful representations of the world.