The acquisition of connectivity patterns in an artificial, three-layer neural circuit for control of human gait was simulated through a backpropagation algorithm. Input signals for the neural network were equivalent to hip, knee, and ankle angles, and to vertical ground reaction forces during walking. Neural network outputs consisted of signals proportional to the activity of five lower limb muscles. Many network input configurations and connectivity restrictions were tested. Data from normal gait were used for network training. Preliminary testing of network responses in unknown environments was effected by presenting the neural networks with actual pathologic data at the input layer and by later investigating network outputs. Results favored restricted connectivity between middle and output layer neurons. Further, explicit recall of recent events was found to improve network behavior.458559
