Modular neuronal assemblies embodied in a closed-loop environment: toward future integration of brains and machines.

Servizi UNIGE per questo articolo(opens in a new window)|Richiedi articolo via Nilde(opens in a new window)|View at Publisher| Export | Download | More... Frontiers in Neural Circuits Issue NOV, 18 November 2012, Pages 1-39 Modular neuronal assemblies embodied in a closed-loop environment: Towards future integration of brains and machines (Article) Tessadori, J.a, Bisio, M.a, Martinoia, S.ab, Chiappalone, M.a a Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, via Morego 30, Genova, 16163, Italy b Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genova, Italy View references (49) Abstract Behaviors, from simple to most complex, require a two-way interaction with the environment and the contribution of different brain areas depending on the orchestrated activation of neuronal assemblies. In this work we present a new hybrid neuro-robotic architecture based on a neural controller bi-directionally connected to a virtual robot implementing a Braitenberg vehicle aimed at avoiding obstacles. The robot is characterized by proximity sensors and wheels, allowing it to navigate into a circular arena with obstacles of different sizes. As neural controller, we used hippocampal cultures dissociated from embryonic rats and kept alive over Micro Electrode Arrays (MEAs) for 3-8 weeks. The developed software architecture guarantees a bi-directional exchange of information between the natural and the artificial part by means of simple linear coding/decoding schemes. We used two different kinds of experimental preparation: 'random' and 'modular' populations. In the second case, the confinement was assured by a PDMS (polydimethylsiloxane) mask placed over the surface of the MEA device, thus defining two populations interconnected via specific microchannels. The main results of our study are: (i) neuronal cultures can be successfully interfaced to an artificial agent; (ii) modular networks show a different dynamics with respect to random culture, both in terms of spontaneous and evoked electrophysiological patterns; (iii) the robot performs better if a reinforcement learning paradigm (i.e. a tetanic stimulation delivered to the network following each collision) is activated, regardless of the modularity of the culture; (iv) the robot controlled by the modular network further enhances its capabilities in avoiding obstacles during the short-term plasticity trial. The developed paradigm offers a new framework for studying, in simplified model systems, neuro-artificial bi-directional interfaces for the development of new strategies for brain-machine interaction