Deep neural networks, in the infinite-width limit, give rise to isotropic Gaussian processes whose spectral and geometric features encode fundamental information about network architectures and activation choices. In this talk, I present a unified theoretical framework, based on three recent works, which reveals a robust three-regime classification emerging across different spectral and geometric descriptors of random networks.