The mapping between nonsymbolic quantities and symbolic numbers lays the foundation for mathematical development in children. However, the neural mechanisms underlying this crucial cognitive bridge remain unclear. Here, we in- vestigate the computational principles governing symbolic- nonsymbolic integration using a biologically inspired neural network trained through developmentally inspired stages. Our investigation reveals that generalization from nonsymbolic to symbolic numerical processing emerges specifically when rep- resentational alignment forms between these numerical for- mats. Notably, this alignment appears to be stronger in cross- format comparison-based mapping compared to direct-label- based mapping. Furthermore, we demonstrate that subsequent symbolic specialization creates a representational divergence that impairs nonsymbolic performance while maintaining the ordinal structure of the mapping. These findings highlight rep- resentational alignment as a fundamental mechanism in nu- merical cognition and suggest that targeted cross-format com- parison tasks may be particularly effective in improving math- ematical learning in children with numerical processing diffi- culties. Keywords: Emergence of number semantics, Representa- tional alignment, Artificial neural network