A new structured network modeling approach based on binary node indexing and Boolean differentials is introduced. Orthogonal ternary vector lists serve as a structured representation of the model's state dynamics. Tensor decomposition methods are enabled by relaxation to continuous Zhegalkin polynomials due to their inherently multilinear nature. A periodic example is used to demonstrate how a low-dimensional state space can provide a large number of linearly independent outputs.