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

A Spiking Central Pattern Generator for the control of a simulated lamprey robot running on SpiNNaker and Loihi neuromorphic boards