  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 in legged robots through a
Spiking Central Pattern Generator. This Spiking Central Pattern Generator
generates different locomotion (rhythmic) patterns which are driven by an
external stimulus, that is, the output of a Force Sensitive Resistor connected
to the robot to provide feedback. The Spiking Central Pattern Generator
consists of a network of five populations of Leaky Integrate-and-Fire 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, the
locomotion of the end robotic platform (any-legged robot) can be adapted to the
terrain by using any sensor as input. The Spiking Central Pattern Generator
with adaptive learning 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
Spiking Central Pattern Generator while the input stimulus varies. To validate
the robustness and adaptability of the Spiking Central Pattern Generator, 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.
