Evolutionary Training of a Biologically Realistic Spino-neuromuscular System
This paper presents a biologically realistic model of the spino-neuromuscular system (SNMS). The model uses a pulse-coded recurrent neural network to control a simulated human-like arm. We use a genetic algorithm to train the network based on a target behavior for the arm. Our goal is to create a useful model for studying the function and behavior of neural pathways in the SNMS. The genetic algorithm is able to train the network to actuate the arm to achieve controlled motion. Our experimental results demonstrate that certain types of feedback pathways are important for controlling muscle movements.