Mammalian locomotion is a complex behavior arising from interaction between neural and biomechanical systems, driven by rhythmic activity originating in the spinal cord. Although it has been extensively studied, the structure of the circuits that produce this behavior remains unknown. One approach to modeling the rhythmic activity is with half-center models, in which there are alternating periods of flexion and extension to coordinate muscle activity. While this approach is sufficient for simple antagonistic muscle pairs, it can be difficult to expand the controller for more complex models with muscle synergies. This work introduces a method of modeling the activity in the spinal cord with a population of neurons exhibiting a continuous cycle of activity, rather than the push-pull of half-centers. To evaluate the effectiveness of this neural model for locomotive behavior, we integrate it with a biomechanical simulation to control the muscle activity in a pair of rat hindlimbs. With this controller, a pair of simulated rat hindlimbs is able to walk on the ground with joint trajectories exhibiting similar features to the animal during locomotion. This model of the spinal cord activity shows promising results on a simple model and demonstrates the ability to be adapted to control more complex biomechanical models with muscle synergies.