Journal article Open Access

Plasticity and Adaptation in Neuromorphic Biohybrid Systems

Richard George; Michela Chiappalone; Michele Giugliano; Timothée Levi; Stefano Vassanelli; Johannes Partzsch; Christian Mayr


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    <subfield code="u">Rehab Technologies, Istituto Italiano di Tecnologia, Genova, Italy.</subfield>
    <subfield code="a">Michela Chiappalone</subfield>
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    <subfield code="u">Laboratoire de l'Intégration du Matéeriau au Systéme, University of Bordeaux, Bordeaux, France. LIMMS/CNRS, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan.</subfield>
    <subfield code="a">Timothée Levi</subfield>
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    <subfield code="u">Department of Biomedical Sciences and Padova Neuroscience Center, University of Padova, Padova, Italy.</subfield>
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    <subfield code="u">Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany</subfield>
    <subfield code="a">Johannes Partzsch</subfield>
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    <subfield code="u">Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany</subfield>
    <subfield code="a">Richard George</subfield>
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    <subfield code="a">Plasticity and Adaptation in Neuromorphic Biohybrid Systems</subfield>
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    <subfield code="a">&lt;p&gt;Abstract.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;Neuromorphic systems take inspiration from the principles of biological information processing to form hardware platforms that enable the large-scale implementation of neural networks. The recent years have seen both advances in the theoretical aspects of spiking neural networks for their use in classification and control tasks and a progress in electrophysiological methods that is pushing the frontiers of intelligent neural interfacing and signal processing technologies. At the forefront of these new technologies, artificial and biological neural networks are tightly coupled, offering a novel &amp;quot;biohybrid&amp;quot; experimental framework for engineers and neurophysiologists. Indeed, biohybrid systems can constitute a new class of neuroprostheses opening important perspectives in the treatment of neurological disorders. Moreover, the use of biologically plausible learning rules allows forming an overall fault-tolerant system of co-developing subsystems. To identify opportunities and challenges in neuromorphic biohybrid systems, we discuss the field from the perspectives of neurobiology, computational neuroscience, and neuromorphic engineering.&lt;/p&gt;</subfield>
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