Locomotion is an evolutionary adaptation that allows animals to move in 3-D space. The way that mammalian locomotion is controlled has been studied for generations. It remains unclear how the neuronal network that controls locomotion is structured and how the mammalian locomotor network keeps balance in the face of a changing environment. In this body of research, we build mathematical models of locomotion and fit our models to experimental data of walking cats to gain understanding of network connectivity and of balance control. Specifically, we test the biological plausibility of a particular connectivity of the mammalian locomotor network by matching network activity to phases of walking in different experimental conditions. We gain understanding of balance control with an inverted pendulum model that fits the center of mass oscillations during walking in different experimental conditions.