A central question in cognitive science is how to reconcile connectionist and symbolic models of the mind (e.g., Fodor & Pylyshyn 1988, Smolensky & Legendre 2006). Attempts have been made to bridge these competing schools of thought by showing how compositional structure can emerge in continuous vector representations (e.g., Manning et al. 2020). A key example is Mikolov et al. (2013), who demonstrated that word embeddings learned by a neural network encode semantic structure: subtracting the vector “man” from “king” and adding “woman” approximates “queen” (i.e., king - man + woman ≈ queen). Our work moves up one level of abstraction, from representations to functions. We analyze whether entire networks display emergent compositional structure by treating a trained network as a single vector (obtained by concatenating the network’s parameters) encoding its function. We show that these parameter vectors can be recomposed through simple additive analogies to create networks with new functions.