Solitonic Construction of Artificial Neural Networks from Nonlinear Field Theory

Abstract

We present a field-theoretic construction of a class of artificial neural networks from solitonic degrees of freedom in nonlinear scalar field theory. The purpose is not to rename a standard neural layer in the language of solitons, but to start from a continuum action, restrict the theory to a nonperturbative sector containing localized stable solutions, perform a collective-coordinate reduction, and derive the neural layer as the finite-dimensional input-output map of the reduced solitonic dynamics. In this construction, the computational unit is a projected collective coordinate of a localized field configuration rather than an elementary point variable; the activation function is the solitonic response profile or scattering map; the weight matrix is the Hessian or overlap matrix of an effective interaction energy among solitons; the bias is induced by external sources, vacuum asymmetry, or boundary forcing; and depth is a discrete evolution parameter on the solitonic moduli space. We develop the construction explicitly for the \(ϕ^4\) kink, where the \(\tanh\) activation and the logistic sigmoid arise from the kink profile, and then derive the multilayer feedforward form from an operator-splitting approximation to collective-coordinate gradient flow. We emphasize the novelty criterion: the neural architecture is obtained only after specifying the field action, the solitonic ansatz, the moduli-space metric, the interaction functional, and the projection map. The result is a controlled route from nonlinear field theory to neural-network structure, with robustness tied to the energetic and topological stability of the solitonic sector.