The last decade has seen the advent of brain computer interfaces able to extract precise motor intentions from cortical activity of human subjects. It is possible to convert captured motor intentions into movement through coordinated, artificially induced, neuromuscular stimulation using peripheral nerve interfaces. Our lab has developed and tested a new type of peripheral nerve electrode called the Macro-Sieve electrode which exhibits excellent chronic stability and recruitment selectivity. Work presented in this thesis uses computational modeling to study the interaction between Macro-Sieve electrodes and regenerated peripheral nerves. It provides a detailed understanding of how regenerated fibers, both on an individual level and on a population level respond differently to functional electrical stimulation compared to non-disrupted axons. Despite significant efforts devoted to developing novel regenerative peripheral interfaces, the degree of spatial clustering between functionally related fibers in regenerated nerves is poorly understood. In this thesis, bioelectrical modeling is also used to predict the degree of topographical organization in regenerated nerve trunks. In addition, theoretical limits of the recruitment selectivity of the device is explored and a set of optimal stimulation paradigms used to selectively activate fibers in different regions of the nerve are determined. Finally, the bioelectrical model of the interface/nerve is integrated with a biomechanical model of the macaque upper limb to study the feasibility of using macro-sieve electrodes to achieve upper limb mobilization
