Structural plasticity for neuromorphic networks with electropolymerized dendritic PEDOT connections - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Nature Communications Année : 2023

Structural plasticity for neuromorphic networks with electropolymerized dendritic PEDOT connections

Jean Rouat

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.
Fichier principal
Vignette du fichier
Kamila Janzakova_2023_nature communications_s41467-023-43887-8.pdf (2.92 Mo) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte
Licence

Dates et versions

hal-04348649 , version 1 (16-12-2023)

Licence

Identifiants

Citer

Kamila Janzakova, Ismael Balafrej, Ankush Kumar, Nikhil Garg, Corentin Scholaert, et al.. Structural plasticity for neuromorphic networks with electropolymerized dendritic PEDOT connections. Nature Communications, 2023, 14 (1), pp.040202. ⟨10.1038/s41467-023-43887-8⟩. ⟨hal-04348649⟩
74 Consultations
17 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More