Structural plasticity for neuromorphic networks with electropolymerized dendritic PEDOT connections

Kamila Janzakova, Ismael Balafrej, Ankush Kumar, Nikhil Garg, Corentin Scholaert, Jean Rouat, Dominique Drouin, Yannick Coffinier, Sébastien Pecqueur and Fabien Alibart ()
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Kamila Janzakova: Univ. Lille, CNRS, Centrale Lille, Univ. Polytechnique Hauts-de-France, UMR 8520-IEMN
Ismael Balafrej: Université de Sherbrooke
Ankush Kumar: Univ. Lille, CNRS, Centrale Lille, Univ. Polytechnique Hauts-de-France, UMR 8520-IEMN
Nikhil Garg: Univ. Lille, CNRS, Centrale Lille, Univ. Polytechnique Hauts-de-France, UMR 8520-IEMN
Corentin Scholaert: Univ. Lille, CNRS, Centrale Lille, Univ. Polytechnique Hauts-de-France, UMR 8520-IEMN
Jean Rouat: Université de Sherbrooke
Dominique Drouin: Université de Sherbrooke
Yannick Coffinier: Univ. Lille, CNRS, Centrale Lille, Univ. Polytechnique Hauts-de-France, UMR 8520-IEMN
Sébastien Pecqueur: Univ. Lille, CNRS, Centrale Lille, Univ. Polytechnique Hauts-de-France, UMR 8520-IEMN
Fabien Alibart: Univ. Lille, CNRS, Centrale Lille, Univ. Polytechnique Hauts-de-France, UMR 8520-IEMN

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: Abstract 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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43887-8

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DOI: 10.1038/s41467-023-43887-8

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