We developed physiologically relevant, neural networks to model time-varying neuronal population operations in the motor cortex and spinal cord, dealing with movements in space. We also developed a model of the interactions between these two networks dealing with generating time-varying motoneuronal outputs for movements in space. The novelty of our approach consisted in (a) the realistic nature of the elements in our networks, (b) the massive and asymmetric interconnectivity among network elements, (c) the physiologically relevant design of the networks, including the communication by spike trains among network elements and rules of connectivity based on experimental findings, (d) the dynamical behavior of the networks, and (e) the time-varying performance of the networks. Finally, we were able to reliably decode and transform the neuronal ensemble activity recorded in behaving animals for controlling an simulated arm. This demonstration suggests that the use of biologically inspired neural networks to transform raw cortical signals into the motor output of a multijoint artificial limb is both feasible and practical time-varying performance of the networks.