In vitro large-scale experimental and theoretical studies for the realization of bi-directional brain-prostheses.

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  • 24 May 2013
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Authors: Paolo Bonifazi, Francesco Difato, Paolo Massobrio, Gian Luca Breschi2, Valentina Pasquale2, Timothée Levi4, Miri Goldin1, Yannick Bornat4, Mariateresa Tedesco3, Marta Bisio2, Sivan Kanner5, Ronit Galron5, Jacopo Tessadori2, Stefano Taverna2* and Michela Chiappalone2*

§Equal contribution; *Equal senior contribution.

 

Brain-machine interfaces (BMI) were born to control “actions from thoughts” in order to recover motor capability of patients with impaired functional connectivity between the central and peripheral nervous system. The final goal of our studies is the development of a new proof-of-concept BMI—a neuromorphic chip for brain repair—to reproduce the functional organization of a damaged part of the central nervous system. To reach this ambitious goal, we implemented a multidisciplinary “bottom-up” approach in which in vitro networks are the paradigm for the development of an in silico model to be incorporated into a neuromorphic device. In this paper we present the overall strategy and focus on the different building blocks of our studies: (i) the experimental characterization and modeling of “finite size networks” which represent the smallest and most general self-organized circuits capable of generating spontaneous collective dynamics; (ii) the induction of lesions in neuronal networks and the whole brain preparation with special attention on the impact on the functional organization of the circuits; (iii) the first production of a neuromorphic chip able to implement a real-time model of neuronal networks. A dynamical characterization of the finite size circuits with single cell resolution is provided. A neural network model based on Izhikevich neurons was able to replicate the experimental observations. Changes in the dynamics of the neuronal circuits induced by optical and ischemic lesions are presented respectively for in vitro neuronal networks and for a whole brain preparation. Finally the implementation of a neuromorphic chip reproducing the network dynamics in quasi-real time (10 ns precision) is presented.