Central pattern generator model using spiking neural networks

Abstract

are essential for survival. The neural locomotor pathways contain the central pattern generator (CPG), a network of neurons embedded into the spinal cord and generating dynamic output for walking and running. Even though there are multiple formulations of the CPG, from coupled oscillators to complex networks of Hodgkin-Huxley neurons, the optimal choice of model implementation depends on its application. The choice of a formulation is often described as the trade-off between complexity and the level of details in the model’s function. However, the advantages between different formulations have not been established. Recently, the spiking neural networks (SNN) have gained popularity as a biological analog for neural dynamics that uses methodology developed for artificial neural networks. This formulation uses spiking frequency instead of rate signals to accomplish dynamic computations with the integrate-and-fire neurons. In this study1, we aimed to create the framework for comparing a versatile CPG rate model and its implementation with the model build with SNN. We used a neuromorphic software package (Nengo) to develop and validate a bilateral CPG model’s structural and functional details based on the halfcenter oscillators. The spiking model shows similar precision for calculating the empirical phase-duration characteristics of gait in cats as the rate model, and it also reproduces the linear relationship between the CPG input and the empirical limb speed of forward progression. While the phase characteristic was used to optimize neural dynamics, the input relationship with the limb speed is the product of the model structure. Furthermore, the spiking model has increased tolerance to temporal noise, and it can withstand some structural damage. The spiking and rate models require further comparative analysis. Overall, the development of adaptable spiking models could help integrate the biomimetic components within the control systems for assistive robotics and electrical stimulation devices to rehabilitate locomotion after central and peripheral injuries.