Neural networks controlling wrist movements

In monkeys performing a step-tracking task, the discharge patterns of forearm motor units and connected premotoneuronal cells in the cortex and red nucleus (identified by postspike facilitation of EMG) fall into characteristic classes: tonic, phasic-tonic, decrementing, etc. The authors used dynamic neural network models incorporating these discharge patterns to investigate networks that could transform a step input of target position to the observed discharge patterns of flexor and extensor motoneurons. These networks have interconnected hidden units with either excitatory or inhibitory connections to each other and to the motoneurons. The activity of many hidden units resembles discharge patterns that are observed in monkey recordings. The network solutions typically involve preferential connectivity within synergistic groups and often include reciprocal inhibition of antagonists. A network trained on a specific input step level does not necessarily produce a proportional output for other step sizes; however, the networks can be trained to generate motor responses proportional to a target step size. The role of the hidden units can also be investigated by selective lesions or stimulation