@ARTICLE{10.21494/ISTE.OP.2018.0269, TITLE={Real‐time biomimetic Central Pattern Generators (CPG) in FPGA for biohybrid experiments}, AUTHOR={Matthieu Ambroise, Sébastien Joucla, Blaise Yvert, Sylvain Saïghi, Timothée Levi, }, JOURNAL={Cognitive Engineering}, VOLUME={1}, NUMBER={Issue 1}, YEAR={2017}, URL={https://openscience.fr/Real-time-biomimetic-Central-Pattern-Generators-CPG-in-FPGA-for-biohybrid}, DOI={10.21494/ISTE.OP.2018.0269}, ISSN={2517-6978}, ABSTRACT={Hybridization is a technique that consists in interconnecting a network of biological neurons and a network of artificial neurons. It is used in neuroscience research and for therapeutic purposes. The long-term goal is to replace damaged neural networks by these artificial systems. These require the development of models of neurons whose electrical activity is similar to the activity of living biological networks. This correspondence allows to produce an adequate stimulation in order to restore the desired neural function. In this paper, digital artificial neural network with a configurable architecture has been designed. This network of artificial neurons emulates the activity of CPGs (Central Pattern Generator), at the origin of the locomotion. This activity triggers a series of stimulations on an injured spinal cord and thus recreates the previously locomotion. These results are a first step toward hybrid artificial/biological solutions based on electrical micro-stimulation for the restoration of lost function in the injured CNS (like locomotion).}}