Hybrid living-artificial neural networks are an efficient and adaptable experimental support to explore the dynamics and the adaptation process of biological neural systems. We present in this paper an innovative platform performing a real-time closed-loop between a cultured neural network and an artificial processing unit like a robotic interface. The system gathers bioware, hardware, and software components and ensures the closed-loop data processing in less than 50 micros. We detail here the system components and compare its performances to a recent commercial platform.
Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Conference
%0 Journal Article
%1 Bontorin:2007p45778
%A Bontorin, G
%A Renaud, S
%A Garenne, A
%A Alvado, L
%A Masson, G Le
%A Tomas, J
%D 2007
%J Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Conference
%K (Computer), Analysis, Animals, Automated, Biomimetics, Bionics, Cells: Computer Computer-Assisted, Cultured, Cybernetics Design, Equipment Failure Feedback, Integration, Nerve Net, Networks Neural Pattern Processing: Rats, Recognition: Robotics, Signal Systems Systems,
%P 3004--7
%R 10.1109/IEMBS.2007.4352961
%T A real-time closed-loop setup for hybrid neural networks
%V 2007
%X Hybrid living-artificial neural networks are an efficient and adaptable experimental support to explore the dynamics and the adaptation process of biological neural systems. We present in this paper an innovative platform performing a real-time closed-loop between a cultured neural network and an artificial processing unit like a robotic interface. The system gathers bioware, hardware, and software components and ensures the closed-loop data processing in less than 50 micros. We detail here the system components and compare its performances to a recent commercial platform.
@article{Bontorin:2007p45778,
abstract = {Hybrid living-artificial neural networks are an efficient and adaptable experimental support to explore the dynamics and the adaptation process of biological neural systems. We present in this paper an innovative platform performing a real-time closed-loop between a cultured neural network and an artificial processing unit like a robotic interface. The system gathers bioware, hardware, and software components and ensures the closed-loop data processing in less than 50 micros. We detail here the system components and compare its performances to a recent commercial platform.},
added-at = {2009-11-12T16:21:13.000+0100},
affiliation = {IMS Laboratory - ENSEIRB University of Bordeaux1, 351 cours de la Lib{\'e}ration, F-33405 Talence, France. guilherme.bontorin@ims-bordeaux.fr},
author = {Bontorin, G and Renaud, S and Garenne, A and Alvado, L and Masson, G Le and Tomas, J},
biburl = {https://www.bibsonomy.org/bibtex/29f3063f114ea2a5844100e6d3969439c/fdiehl},
date-added = {2009-09-23 23:12:02 +0200},
date-modified = {2009-11-10 09:44:52 +0100},
description = {bib-komplett},
doi = {10.1109/IEMBS.2007.4352961},
interhash = {2e4ae2ee3f3acb47d2d0d1246c00404a},
intrahash = {9f3063f114ea2a5844100e6d3969439c},
journal = {Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Conference},
keywords = {(Computer), Analysis, Animals, Automated, Biomimetics, Bionics, Cells: Computer Computer-Assisted, Cultured, Cybernetics Design, Equipment Failure Feedback, Integration, Nerve Net, Networks Neural Pattern Processing: Rats, Recognition: Robotics, Signal Systems Systems,},
language = {eng},
local-url = {file://localhost/Neurobio/Papers/18002627.pdf},
month = Jan,
pages = {3004--7},
pmid = {18002627},
rating = {0},
timestamp = {2009-11-12T16:21:34.000+0100},
title = {A real-time closed-loop setup for hybrid neural networks},
uri = {papers://7B65697B-E216-4648-8A41-C67830C0DC73/Paper/p45778},
volume = 2007,
year = 2007
}