- Titel
- Plasticity and Adaptation in Neuromorphic Biohybrid Systems
- Untertitel
- Review
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-898973
- Übersetzter Titel (DE)
- Plastizität und Anpassung in neuromorphen Biohybridsystemen : Rückblick
- Quellenangabe
- iScience
Erscheinungsjahr: 2020
Jahrgang: 23
Heft: 10
E-ISSN: 2589-0042
Artikelnummer: 101589 - Abstract (EN)
- 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 “biohybrid” 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.
- Andere Ausgabe
- Link zum Artikel, der zuerst in der Zeitschrift „iScience” erschienen ist.
DOI: 10.1016/j.isci.2020.101589 - Freie Schlagwörter (DE)
- Biohybrid, neuromorph, geschlossener Kreislauf, Anpassung, Plastizität
- Freie Schlagwörter (EN)
- Biohybrid, Neuromorphic, Closed-Loop, Adaptation, Plasticity
- Klassifikation (DDC)
- 050
- Verlag
- Elsevier, Amsterdam ; Boston ; London ; New York ; Oxford ; Paris ; Philadelphia ; San Diego ; St. Louis
- Förder- / Projektangaben
- European Commission (EC)
H2020 | RIA
A SYnaptically connected brain-silicon Neural Closed-loop Hybrid system
(SYNCH)
ID: 824162 - European Commission (EC)
H2020 | RIA
Ionic Neuromodulation For Epilepsy Treatment
(IN-FET)
ID: 862882 - Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-898973
- Veröffentlichungsdatum Qucosa
- 21.02.2024
- Dokumenttyp
- Artikel
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY-NC-ND 4.0