From neuromorphic to neurohybrid: transition from the emulation to the integration of neuronal networks
The computation of the brain relies on the highly efficient communication among billions of neurons. Such efficiency derives from the brain’s plastic and reconfigurable nature, enabling complex computations and maintenance of vital functions with a remarkably low power consumption of only ∼20 W. Fir...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | Neuromorphic Computing and Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2634-4386/acc683 |
| _version_ | 1797826800601006080 |
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| author | Ugo Bruno Anna Mariano Daniela Rana Tobias Gemmeke Simon Musall Francesca Santoro |
| author_facet | Ugo Bruno Anna Mariano Daniela Rana Tobias Gemmeke Simon Musall Francesca Santoro |
| author_sort | Ugo Bruno |
| collection | DOAJ |
| description | The computation of the brain relies on the highly efficient communication among billions of neurons. Such efficiency derives from the brain’s plastic and reconfigurable nature, enabling complex computations and maintenance of vital functions with a remarkably low power consumption of only ∼20 W. First efforts to leverage brain-inspired computational principles have led to the introduction of artificial neural networks that revolutionized information processing and daily life. The relentless pursuit of the definitive computing platform is now pushing researchers towards investigation of novel solutions to emulate specific brain features ( such as synaptic plasticity) to allow local and energy efficient computations. The development of such devices may also be pivotal in addressing major challenges of a continuously aging world, including the treatment of neurodegenerative diseases. To date, the neuroelectronics field has been instrumental in deepening the understanding of how neurons communicate, owing to the rapid development of silicon-based platforms for neural recordings and stimulation. However, this approach still does not allow for in loco processing of biological signals. In fact, despite the success of silicon-based devices in electronic applications, they are ill-suited for directly interfacing with biological tissue. A cornucopia of solutions has therefore been proposed in the last years to obtain neuromorphic materials to create effective biointerfaces and enable reliable bidirectional communication with neurons. Organic conductive materials in particular are not only highly biocompatible and able to electrochemically transduce biological signals, but also promise to include neuromorphic features, such as neuro-transmitter mediated plasticity and learning capabilities. Furthermore, organic electronics, relying on mixed electronic/ionic conduction mechanism, can be efficiently coupled with biological neural networks, while still successfully communicating with silicon-based electronics. Here, we envision neurohybrid systems that integrate silicon-based and organic electronics-based neuromorphic technologies to create active artificial interfaces with biological tissues. We believe that this approach may pave the way towards the development of a functional bidirectional communication between biological and artificial ‘brains’, offering new potential therapeutic applications and allowing for novel approaches in prosthetics. |
| first_indexed | 2024-04-09T12:38:02Z |
| format | Article |
| id | doaj.art-d1d0dc872b0b4c3cb27852aa77ec0fcb |
| institution | Directory Open Access Journal |
| issn | 2634-4386 |
| language | English |
| last_indexed | 2024-04-09T12:38:02Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Neuromorphic Computing and Engineering |
| spelling | doaj.art-d1d0dc872b0b4c3cb27852aa77ec0fcb2023-05-15T06:27:49ZengIOP PublishingNeuromorphic Computing and Engineering2634-43862023-01-013202300210.1088/2634-4386/acc683From neuromorphic to neurohybrid: transition from the emulation to the integration of neuronal networksUgo Bruno0https://orcid.org/0000-0003-0419-6541Anna Mariano1https://orcid.org/0000-0002-3630-5055Daniela Rana2https://orcid.org/0000-0002-7929-1191Tobias Gemmeke3https://orcid.org/0000-0003-1583-3411Simon Musall4https://orcid.org/0000-0002-9461-1042Francesca Santoro5https://orcid.org/0000-0001-7323-9504Tissue Electronics, Istituto Italiano di Tecnologia , 80125 Naples, Italy; Dipartimento di Chimica, Materiali e Produzione Industriale, Università di Napoli Federico II , 80125 Naples, ItalyTissue Electronics, Istituto Italiano di Tecnologia , 80125 Naples, ItalyFaculty of Electrical Engineering and IT, RWTH , Aachen 52074, Germany; Institute of Biological Information Processing—Bioelectronics , IBI-3, Forschungszentrum, Juelich 52428, GermanyFaculty of Electrical Engineering and IT, RWTH , Aachen 52074, GermanyInstitute of Biological Information Processing—Bioelectronics , IBI-3, Forschungszentrum, Juelich 52428, GermanyTissue Electronics, Istituto Italiano di Tecnologia , 80125 Naples, Italy; Faculty of Electrical Engineering and IT, RWTH , Aachen 52074, Germany; Institute of Biological Information Processing—Bioelectronics , IBI-3, Forschungszentrum, Juelich 52428, GermanyThe computation of the brain relies on the highly efficient communication among billions of neurons. Such efficiency derives from the brain’s plastic and reconfigurable nature, enabling complex computations and maintenance of vital functions with a remarkably low power consumption of only ∼20 W. First efforts to leverage brain-inspired computational principles have led to the introduction of artificial neural networks that revolutionized information processing and daily life. The relentless pursuit of the definitive computing platform is now pushing researchers towards investigation of novel solutions to emulate specific brain features ( such as synaptic plasticity) to allow local and energy efficient computations. The development of such devices may also be pivotal in addressing major challenges of a continuously aging world, including the treatment of neurodegenerative diseases. To date, the neuroelectronics field has been instrumental in deepening the understanding of how neurons communicate, owing to the rapid development of silicon-based platforms for neural recordings and stimulation. However, this approach still does not allow for in loco processing of biological signals. In fact, despite the success of silicon-based devices in electronic applications, they are ill-suited for directly interfacing with biological tissue. A cornucopia of solutions has therefore been proposed in the last years to obtain neuromorphic materials to create effective biointerfaces and enable reliable bidirectional communication with neurons. Organic conductive materials in particular are not only highly biocompatible and able to electrochemically transduce biological signals, but also promise to include neuromorphic features, such as neuro-transmitter mediated plasticity and learning capabilities. Furthermore, organic electronics, relying on mixed electronic/ionic conduction mechanism, can be efficiently coupled with biological neural networks, while still successfully communicating with silicon-based electronics. Here, we envision neurohybrid systems that integrate silicon-based and organic electronics-based neuromorphic technologies to create active artificial interfaces with biological tissues. We believe that this approach may pave the way towards the development of a functional bidirectional communication between biological and artificial ‘brains’, offering new potential therapeutic applications and allowing for novel approaches in prosthetics.https://doi.org/10.1088/2634-4386/acc683organic neuromorphicneurohybridorganic electronicsbrain-machine interfacessynaptic plasticity |
| spellingShingle | Ugo Bruno Anna Mariano Daniela Rana Tobias Gemmeke Simon Musall Francesca Santoro From neuromorphic to neurohybrid: transition from the emulation to the integration of neuronal networks Neuromorphic Computing and Engineering organic neuromorphic neurohybrid organic electronics brain-machine interfaces synaptic plasticity |
| title | From neuromorphic to neurohybrid: transition from the emulation to the integration of neuronal networks |
| title_full | From neuromorphic to neurohybrid: transition from the emulation to the integration of neuronal networks |
| title_fullStr | From neuromorphic to neurohybrid: transition from the emulation to the integration of neuronal networks |
| title_full_unstemmed | From neuromorphic to neurohybrid: transition from the emulation to the integration of neuronal networks |
| title_short | From neuromorphic to neurohybrid: transition from the emulation to the integration of neuronal networks |
| title_sort | from neuromorphic to neurohybrid transition from the emulation to the integration of neuronal networks |
| topic | organic neuromorphic neurohybrid organic electronics brain-machine interfaces synaptic plasticity |
| url | https://doi.org/10.1088/2634-4386/acc683 |
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