Cyclic movements, for instance in locomotion, can be driven by cyclic neural activity, so called Central pattern generators (CPGs). CPGs have been observed at the spinal cord level and even in neural networks isolated from the brain and from sensorimotor feedback. The speed of CPG controlled locomotion, including shift of gait type, can be controlled by simple high level signals, such as tonic electrical stimulation of the brain stem. At the spinal cord level, sensorimotor feedback is integrated to fine tune the motor signals to the environment.
To integrate higher level commands with sensor/body feedback for motor signal generation, we are developing a control system based on reservoir computing (see figure below). The reservoir consists of populations of spiking neurons that are randomly connected. Inputs to the reservoir are on the one hand a generic periodic signal (modeling the high level command), and on the other hand sensor/body feedback from the robotic body that is to be controlled. The reservoir computing paradigm allows for straightforward extraction of desired motor signals from the resulting reservoir activity.
In a future blog post the physical and virtual robotic platform to conduct these experiments will be presented.