(swing or stance movement) to execute for that specific leg [@schilling_walknet_2013]. The overall behavior of the system emerges from local interactions between these controllers. For an outside observer, such a system produces a form of stable temporal patterns (as shown in Fig. \[fig\_curve\], top). Importantly, these are highly adaptive. When the walking system is externally disturbed (either through a spatial disturbance as when walking over uneven terrain or a temporal disturbance when a leg is not finding a foothold or is actively displaced) the observed pattern might look very different, but overall the interacting modules together compensate for the disturbance and overall the system quickly settles back towards a well-organized walking pattern and stable walking without tumbling. In the following part, we will argue that it might be more appropriate to understand such motor control architectures as active, interacting control structures, instead of trying to find specific representations that mediate between different modules. While such concurrent control circuits and their dynamic interactions may be more difficult to analyze and understand, they might offer a better starting point for approaching adaptivity, especially in more complex hierarchically organized systems. Instead of enforcing a particular representation scheme, the interaction between the different (simple) 