  Central Pattern Generators (CPGs) models have been long used to investigate
both the neural mechanisms that underlie animal locomotion as well as a tool
for robotic research. In this work we propose a spiking CPG neural network and
its implementation on neuromorphic hardware as a means to control a simulated
lamprey model. To construct our CPG model, we employ the naturally emerging
dynamical systems that arise through the use of recurrent neural populations in
the Neural Engineering Framework (NEF). We define the mathematical formulation
behind our model, which consists of a system of coupled abstract oscillators
modulated by high-level signals, capable of producing a variety of output
gaits. We show that with this mathematical formulation of the Central Pattern
Generator model, the model can be turned into a Spiking Neural Network (SNN)
that can be easily simulated with Nengo, an SNN simulator. The spiking CPG
model is then used to produce the swimming gaits of a simulated lamprey robot
model in various scenarios. We show that by modifying the input to the network,
which can be provided by sensory information, the robot can be controlled
dynamically in direction and pace. The proposed methodology can be generalized
to other types of CPGs suitable for both engineering applications and
scientific research. We test our system on two neuromorphic platforms,
SpiNNaker and Loihi. Finally, we show that this category of spiking algorithms
shows a promising potential to exploit the theoretical advantages of
neuromorphic hardware in terms of energy efficiency and computational speed.
