Abstract

There is an increasing need for researchers to develop a greater understanding of the neuromuscular system. The medical treatment of many diseases and disorders depends on physicians and practitioners having specific knowledge of how damage to certain spinal pathways can affect motor control. To that end, an important step in increasing our understanding of the spino-neuromuscular system (SNMS) is to develop a model in which researchers can conduct controlled virtual experiments within the spinal cord. This dissertation develops such a model while addressing limitations in current modeling methods of neuromuscular systems. This dissertation also shows that evolutionary algorithms train robust and stable SNMS models that yield key biological behaviors. This type of model is widely applicable in areas such as evolutionary robotics, neuroprosthetics, and modeling neuromuscular diseases since all these areas investigate the importance of specific components in biological or biologically related systems.