Topological genomics offers a novel framework for understanding how genomic structures influence neural circuit differentiation, integrating principles of differential topology and statistical mechanics homology. This approach examines how genomic topological invariants, such as persistent loops and Betti numbers, correspond to functional transformations in neural circuits. By employing persistent homology to analyze genomic interaction maps and differential topology to model neural circuit formation, we identify key transitions that govern differentiation. Statistical mechanics complements this framework by modeling energy landscapes and phase transitions that reflect the emergent properties of neural architectures. Together, these interdisciplinary methods elucidate the role of topological features in genetic regulation and neural circuit specialization, with implications for neurodevelopment, pathology, and artificial intelligence.