TY  - Type of reference 
TI  - Real‐time biomimetic Central Pattern Generators (CPG) in FPGA for biohybrid experiments 
AU  - Matthieu Ambroise 
AU  - Sébastien Joucla 
AU  - Blaise Yvert 
AU  - Sylvain Saïghi 
AU  - Timothée Levi 
AB  - 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). 
DO  - 10.21494/ISTE.OP.2018.0269 
JF  - Cognitive Engineering 
KW  - FPGA,  neural network,  Izhikevich,  spinal cord,  hybrid experiment, FPGA,  réseau de neurones,  Izhikevich,  colonne vertébrale,  expérimentation hybride,  
L1  - http://www.openscience.fr/IMG/pdf/iste_ingecogv1n1_1.pdf 
LA  - en 
PB  - ISTE OpenScience 
DA  - 2017/09/28 
SN  - 2517-6978 
TT  - Central Pattern Generators (CPG) biomimétiques en temps-réel sur FPGA pour des expérimentations biohybrides 
UR  - http://www.openscience.fr/Real-time-biomimetic-Central-Pattern-Generators-CPG-in-FPGA-for-biohybrid 
IS  - Issue 1 
VL  - 1 
ER  -