Water waves can help aquatic animals to distinguish between predators and prey. Previous studies suggest that leech sensory receptors have evolved to respond to relevant wave frequencies. While these studies examined how sensory information affects animal behavior, the underlying neural processing remains unclear. In this study, we present a model that mimics leech goal seeking behavior using an agent-based simulation. Our model uses neural fields, a Winner-Take-All framework from computational neuroscience, to process sensory data. A simulated leech was placed in a simulated environment containing artificial water waves. A distributed sensor array around the agent detected the wave motion, which was then processed via a computational neuroscience approach called a neural field. This sensory information was used to compute motion directions. Modeled behavioral data aligned with data from previous animal experiments. Our model can complement animal experiments by allowing us to pose questions that would be challenging to address directly in an animal. Also, our results may provide insights into novel processing approaches that can be leveraged by man-made sensory systems to process data from multiple sensors.