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Computational Intelligence and Neuroscience
Volume 2007, Article ID 12725, 13 pages
Research Article

Connecting Neurons to a Mobile Robot: An In Vitro Bidirectional Neural Interface

1Neuroengineering and Bio-nanotechnology Group, Department of Biophysical and Electronic Engineering (DIBE), University of Genova, Via Opera Pia 11a, Genova 16145, Italy
2NeuroLab, Department of Informatics Systems and Telematics (DIST), Via Opera Pia 13, Genova 16145, Italy
3Center for Neuroscience and Neuroengineering “Massimo Grattarola”, University of Genova, Genova, Viale Benedetto XV, 3 16132, Italy

Received 27 December 2006; Revised 4 April 2007; Accepted 18 June 2007

Academic Editor: Fabio Babiloni

Copyright © 2007 A. Novellino et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason x201C;embodiment” represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA), to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses.