Structural plasticity for neuromorphic networks with electropolymerized dendritic PEDOT connections.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
08 Dec 2023
08 Dec 2023
Historique:
received:
17
02
2023
accepted:
22
11
2023
medline:
9
12
2023
pubmed:
9
12
2023
entrez:
8
12
2023
Statut:
epublish
Résumé
Neural networks are powerful tools for solving complex problems, but finding the right network topology for a given task remains an open question. Biology uses neurogenesis and structural plasticity to solve this problem. Advanced neural network algorithms are mostly relying on synaptic plasticity and learning. The main limitation in reconciling these two approaches is the lack of a viable hardware solution that could reproduce the bottom-up development of biological neural networks. Here, we show how the dendritic growth of PEDOT:PSS-based fibers through AC electropolymerization can implement structural plasticity during network development. We find that this strategy follows Hebbian principles and is able to define topologies that leverage better computing performances with sparse synaptic connectivity for solving non-trivial tasks. This approach is validated in software simulation, and offers up to 61% better network sparsity on classification and 50% in signal reconstruction tasks.
Identifiants
pubmed: 38065951
doi: 10.1038/s41467-023-43887-8
pii: 10.1038/s41467-023-43887-8
pmc: PMC10709651
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
8143Subventions
Organisme : EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)
ID : GA 773228
Organisme : Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (NSERC Canadian Network for Research and Innovation in Machining Technology)
ID : 559730
Informations de copyright
© 2023. The Author(s).
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