The transformation of non-image data into visual representations suitable for deep learning remains a challenging frontier in machine learning. Many existing methods map tabular data to images via global feature-space layouts, which can reduce instance-specific nuance. We propose Topological Activation Maps (TAMs), a new framework that couples global feature topology with per-instance activations for more faithful and interpretable data transformations. TAMs use a two-phase embedding: (i) kernel projection and Self-Organizing Map training establish a prototype grid capturing global feature relationships; (ii) each sample generates a unique activation map by interacting with this grid through Gaussian-weighted distances. This design preserves both dataset-level structure and sample-specific characteristics. On benchmark classification datasets, TAMs deliver competitive or superior accuracy compared with strong ensemble baselines while offering a complementary perspective on interpretability.