Movement Generation and Control with Generic Neural Microcircuits ⋆ Prashant Joshi and Wolfgang Maass Institute for Theoretical Computer Science Technische Universitaet Graz A-8010 Graz, Austria {joshi,maass}@igi.tugraz.at Abstract. Simple linear readouts from generic neural microcircuit mod- els can be trained to generate and control basic movements, e.g., reach- ing with an arm to various target points. After suitable training of these readouts on a small number of target points; reaching movements to nearby points can also be generated. Sensory or proprioceptive feed- back turns out to improve the performance of the neural microcircuit model, if it arrives with a significant delay of 25 to 100 ms. Further- more, additional feedbacks of “prediction of sensory variables” are shown to improve the performance significantly. Existing control methods in robotics that take the particular dynamics of sensors and actuators into account(“embodiment of robot control”) are taken one step further with this approach which provides methods for also using the “embodiment of computation”, i.e. the inherent dynamics and spatial structure of neural circuits, for the design of robot movement controllers. 1 Introduction This article demonstrates that simple linear readouts from generic neural mi- crocircuit models consisting of spiking neurons and dynamic synapses can be trained to generate and control rather complex movements. Using biologically realistic neural circuit models to generate and control movements is not so easy, since these models are made of spiking neurons and dynamic synapses which exhibit a rich inherent dynamics on several temporal scales. This tends to be in conflict with movement control tasks that require focusing on a relatively slow time scale. Preceding work on movement control, has drawn attention to the need of taking the “embodiment of motor systems”, i.e. the inherent dynamics of sensors and actuators into account. This approach is taken one step further in this article, as it provides a method for also taking into account the “embodiment of neural computation”, i.e. the inherent dynamics and spatial arrangement of neural circuits that control the movements. Hence it may be seen as a first step in ⋆ The work was partially supported by the Austrian Science Fond FWF, project # P15386. A.J. Ijspeert et al. (Eds.): BioADIT 2004, LNCS 3141, pp. 258–273, 2004. c  Springer-Verlag Berlin Heidelberg 2004