Neuromorphic adaptive spiking CPG towards bio-inspired locomotion

In recent years, locomotion mechanisms exhibited by vertebrate animals have been the inspiration for the improvement in the performance of robotic systems. These mechanisms include the adaptability of their locomotion to any change registered in the environment through their biological sensors. In this regard, we aim to replicate such kind of adaptability through a sCPG. This sCPG generates different locomotion (rhythmic) patterns which are driven by an external stimulus, that is, the output of a FSR sensor to provide feedback. The sCPG consists of a network of five populations of LIF neurons designed with a specific topology in such a way that the rhythmic patterns can be generated and driven by the aforementioned external stimulus. Therefore, eventually, the locomotion of an end robotic platform could be adapted to the terrain by using any sensor as input. The sCPG with adaptation has been numerically validated at software and hardware level, using the Brian 2 simulator and the SpiNNaker neuromorphic platform for the latest. In particular, our experiments clearly show an adaptation in the oscillation frequencies between the spikes produced in the populations of the sCPG while the input stimulus varies. To validate the robustness and adaptability of the sCPG, we have performed several tests by variating the output of the sensor. These experiments were carried out in Brian 2 and SpiNNaker; both implementations showed a similar behavior with a Pearson correlation coefficient of 0.905.