While decoded peripheral neural activity recorded by a longitudinal intrafascicular electrode (LIFE) from amputees can be used to control a single degree-of-freedom (DOF) robot arm, recording and decoding of signals from multiple LIFEs for multi-DOF motor control has not yet been achieved. We developed a tool to facilitate the development of online decoding algorithms for this task. The tool translates motor intent signals to simulated neural recordings from many LIFEs. Motor intent signals drive a pool of simulated motor neurons with various spike shapes, recruitment characteristics, and firing rate properties. Each LIFE records a weighted sum of a subset of simulated motor neuron activity patterns. Furthermore, we show by using simulated data sets that simple decoding schemes such as spike counting is a 'good' estimator of motor intents.