A real-time FPGA implementation of a biologically inspired central pattern generator network

We engineer a basic CPG with conductance-based KomendantovKononenko neuron model.We propose a multiplier-less FPGA implementation method with low hardware cost.The neural dynamics are highlighted in the design in a biorealistic manner.We employ piecewise linearization method to obtain the reduced neuron model. Central pattern generators (CPGs) functioning as biological neuronal circuits are responsible for generating rhythmic patterns to control locomotion. In this paper, a biologically inspired CPG composed of two reciprocally inhibitory neurons was implemented on a reconfigurable FPGA with real-time computational speed and considerably low hardware cost. High-accuracy neural circuit implementation can be computationally expensive, especially for a high-dimensional conductance-based neuron model. Thus, we aimed to present an efficient multiplier-less hardware implementation method for the investigation of real-time hardware CPG (hCPG) networks. In order to simplify the hardware implementation, a modified neuron model without nonlinear parts was given to decrease the complexity of the original model. A simple CPG network involving two chemical coupled neurons was realized which represented the pyloric dilator (PD) and lateral pyloric (LP) neurons in the crustacean pyloric CPG. The implementation results of the hCPG network showed that rhythmic behaviors were successfully reproduced and the resource consumption was dramatically reduced by using our multiplier-less implementation method. The presented FPGA-based implementation of hCPG network with remarkable performance set a prototype for the realization of other large-scale CPG networks and could be applied in bio-inspired robotics and motion rehabilitation for locomotion control.