Excited String States and D-branes from Infinite Width Neural Networks

We explore recent proposal to represent worldsheet string path integrals by integrating over parameters of a wide random-feature neural network whose output is identified with the embedding field $X^μ$. In this paper we extend it focusing on scattering with excited states insertions and for worldsheets with boundaries introducing fixed-feature Gaussian normal-ordering prescription for derivative composites (removing the neural contact term at finite width), and propose realization of mixed Neumann/Dirichlet boundary conditions interpreted as a neural D$p$-brane. As concrete outputs, we derive the sphere four-point integrand with a single $(1,1)$ insertion and the disk four-tachyon amplitude on a D$p$-brane, recovering the expected derivative prefactors, boundary exponents, and momentum-conservation limits after renormalization.