This study proposes a model that combines a realistically scaled neural network made up of pools of spiking neurons, with a musculoskeletal model of the human arm. We used evidence from literature to design topological pools of spinal neurons and their synaptic connections. The spiking output of the motor neuron pools were used as the command signals that generated motor unit forces, and drove joint motion. Feedback information from muscle spindles was relayed to the neural network via monosynaptic and disynaptic pathways. Participant-specific parameters of the combined neuromuscu-loskeletal (NMS) system were then identified from recorded experimental data. The identified NMS model was used to simulate the arm stretch reflex and the results were validated by comparison to an independent recorded dataset. The models and methodology proposed in this study show that large and complex neural systems can be identified in conjunction with the musculoskeletal systems that they control. This additional layer of detail in NMS models has important relevance to the research communities related to rehabilitation robotics and human movement analysis.