In this paper, we present a walking biped, based on principles of mammalian neuromuscular architecture. Walking in mammals is a fluid, dynamical interaction between a central pattern generator, the biomechanics of the body, the environment, and sensory feedback. Our robot is designed based on principles of human leg muscle architecture. We incorporate load detecting force sensors that model Golgi tendon organs in the muscles, as well as foot pressure and joint angle sensors. These sensory feedback sources model those available in the human body. The robot is controlled by a spiking neuron simulation that integrates centrally generated (CPG) with peripheral (reflexive) responses. Using recent understanding of the neurobiology of locomotion, we are able to generate an effective and stable walking pattern using interactions between the biomechanics, CPG, and reflexive responses. The CPG drives overall limb motion at the hips, while phase modulated reflexive responses adapt the pattern of the lower limb to the needs of the step cycle. Load detection by the force sensors in the limb generates propulsive stepping, and controls entrainment of the CPG through positive force feedback. These concepts are important ones for locomotion in mammals that should be considered by roboticists developing walking robots.