Foraging is an embodied cognitive process which balances the search constraints of exploration versus exploitation. As such, foraging strategies and mechanisms offer useful insight into abstract forms of search such as visual search, problem solving, and semantic recall. We performed a series of foraging simulations using artificial neural networks to relate metastable neuronal dynamics to observed foraging behaviors. We show that the velocity and tortuosity of the foraging paths are influenced by metastable neuronal activity, while resource collection is unaffected. These initial results indicate that neuronal metastability may contribute to foraging behaviors but additional mechanisms are needed to optimally exploit environmental resources.