In two phases, we develop increasingly complex neural network models of spinal circuitry that self-organizes into networks with opponent channels for the control of an antagonistic muscle pair. The self-organization is enabled by a Hebbian learning rule operating during spontaneous activity present in the spinal cord. After the self-organized development, the networks enable independent control of the length and tension of the innervated muscles. This allows higher centers to hold joint angle invariant while varying joint stiffness and vice versa. The first network comprises only spontaneous activity generators, motorneurons, and inhibitory interneurons through which the two channels interact. The inhibitory interneurons enhance reciprocal action, and prevent saturation of the motorneuron pools, which is a necessary condition for independent control. In the second network, the neurons in the motorneuron pools obey the size-principle, which, when added by itself, leads to a loss of the desired invariance property. To restore the desired invariance, the second network further incorporated inhibitory interneurons analogous to Renshaw cells. The results obtained from the two models compare favourably with the FLETE-model for spinal circuitry (Bullock and Contreras-Vidal, 1993;Bullock et al., 1992;Bullock and Grossberg, 1991) which, although successful in explaining several phenomena related to motor control, did not self-organize its connection weights. Finally, we suggest ways in which this research could be applied in technology.