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This article discusses analog neural processing structures for artificial locomotion in mechatronic devices. The main inspiration comes from the biological paradigm of the central pattern generator, used to model the neural populations responsible for locomotion planning and control in animals. We start by considering locomotion by legs as a complex spatiotemporal, nonlinear dynamic system, modeled referring to particular types of reaction-diffusion nonlinear partial differential equations. Spatiotemporal phenomena are then obtained by implementing the whole mathematical model on a new reaction-diffusion cellular neural network (CNN) architecture. Wavelike solutions as well as patterns are obtained and are able to induce and control locomotion in some prototypes of biologically inspired walking machines. The CNN structure is subsequently designed using analog circuits; this makes it possible to generate locomotion in real time and also to control transition between several types of locomotion. The methodology presented is applied to the experimental prototype of a hexapod robot.