A bio-inspired neuromuscular model to simulate the neuro-sensorimotor basis for postural-reflex-response in humans

Neuromuscular modeling is a new and popular trend with promising implications to understand the concepts behind various complex biological systems. In this study, a biologically-inspired neuromuscular model that can be used to suggest the neuro-sensorimotor basis behind the posture-reflex-response in humans is proposed. The model is attempting to simulate the rule of the Central Nervous System (CNS) in dealing with the complexity level of the sensorimotor signal flows when performing natural body behavior. Our assumption here is that the CNS deals only with a relatively small but valuable amount of data to process useful information. To fulfill this assumption, input/output signals to/from the model are factorized into two parts through a synergistic system: one part defines the working space (we called synergy weight W, which represents the low-dimensional space of the model), and the other defines the motion in the space (we called neural command C, which represents the high-dimensional space of the model). Thus, leads to a bow-tie-like structure. The questions to be discussed are: what type of learning methodology is suitable to fit with such as synergistic-based model. How this model would effectively reduce the muscles and sensors redundancies and produces a suitable state of information to construct meaningful coordinated movements. Non-negative matrix factorization (NMF) was used to identify the model synergies. Software for interactive musculoskeletal modeling (SIMM) was used to construct, train and validate the proposed model. The adopted task was the human posture-reflex- response to ground lateral perturbations. Data used in this study were collected from four healthy subjects. Results showed that the proposed model was able to produce C-like commands that relatively match the experimental data. We believe that our proposed model can offer a scientific approach to the comprehension of the sensorimotor-neural relationship and learning techniques that may suggest various applications for neural rehabilitation.