  Legged locomotion is a challenging task in the field of robotics but a rather
simple one in nature. This motivates the use of biological methodologies as
solutions to this problem. Central pattern generators are neural networks that
are thought to be responsible for locomotion in humans and some animal species.
As for robotics, many attempts were made to reproduce such systems and use them
for a similar goal. One interesting design model is based on spiking neural
networks. This model is the main focus of this work, as its contribution is not
limited to engineering but also applicable to neuroscience. This paper
introduces a new general framework for building central pattern generators that
are task-independent, biologically plausible, and rely on learning methods. The
abilities and properties of the presented approach are not only evaluated in
simulation but also in a robotic experiment. The results are very promising as
the used robot was able to perform stable walking at different speeds and to
change speed within the same gait cycle.
