Robotics researchers used the simulation framework to speed up the process of robot design and control. A representative hexapod robot, RHex14, was designed to perform the double-tripod gait similar to the gait pattern observed in hexapod insects. This gait pattern allowed inherent locomotion stability. The open-loop controller was further improved by the introduction of Central Pattern Generator (CPG). The biological evidences show that the CPG is responsible for producing the basic gait pattern of real animals15. Bio-inspired robots, such as walking salamander16, swimming fish17 and flying bird18, are controlled by mimicking the machinery of CPG. More details can be found in the review paper on applications of CPG for locomotion control in animals and robots19. However, existing models of CPG are generally constructed as a network of nonlinear oscillators, rather than spiking neurons. The advantage of using nonlinear oscillator is the inherent convergence to limit cycle or point attraction for most oscillator models, thus capable of endogenously generating the gait pattern without the high-level commands. However, the challenge from oscillator-based networks is how to connect the sensory information/high-level commands with the low-level control strategy. Previous method11 used a precomputed look-up table. This inevitably creates a large discretized look-up table. In comparison, real organisms, including insects, use spiking neurons for the general purposes of environmental sensory, information transmission, decision-making and muscle actuation. Such observation inspires this work to explore the solution of using spiking neuron, as the alternative building block to nonlinear oscillator, to construct the neural system.