Modeling, simulation, and implementation issues of CPGs for neuromorphic engineering applications

Abstract

Neuromorphic engineering is a discipline used to develop hardware, which can mimic the characteristics and abilities of biological systems by investigating their physiological structures and data transfer mechanisms. The recent studies about the neuromorphic systems mostly consist of robotic applications whose designs are inspired by Central Pattern Generators (CPGs). CPGs are special neural networks which can produce coordinated rhythmic activity patterns and these rhythmic movements are modeled mathematically, tested with simulation programs and verified by hardware implementations. A reconfigurable hardware platform (Field Programmable Gate Array FPGA) is compatible with numerical simulation tools, allows software control over hardware, has a user-friendly interface and allows real time modifications. Thus, recently, it is preferred in CPG based robotic applications. In this study, the details of the modeling, simulation and implementation stages of several CPG structures are introduced by using a digital reconfigurable hardware platform. In order to show the conceptual learning achievements of these stages and to assess the contribution to the modeling, simulation and implementation skills of the students, a training course has been planned for the undergraduate students at Erciyes University. This process has been held in an educative manner supported by a survey and an experimental examination, so that this training course has been evaluated by the trainees in terms of the advantage, practicality, and challenge.