Analog programing of conducting-polymer dendritic interconnections and control of their morphology.


Journal

Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555

Informations de publication

Date de publication:
25 11 2021
Historique:
received: 22 01 2021
accepted: 05 11 2021
entrez: 26 11 2021
pubmed: 27 11 2021
medline: 27 11 2021
Statut: epublish

Résumé

Although materials and processes are different from biological cells', brain mimicries led to tremendous achievements in parallel information processing via neuromorphic engineering. Inexistent in electronics, we emulate dendritic morphogenesis by electropolymerization in water, aiming in operando material modification for hardware learning. Systematic study of applied voltage-pulse parameters details on tuning independently morphological aspects of micrometric dendrites': fractal number, branching degree, asymmetry, density or length. Growths time-lapse image processing shows spatial features to be dynamically dependent, and expand distinctively before and after conductive bridging with two electro-generated dendrites. Circuit-element analysis and impedance spectroscopy confirms their morphological control in temporal windows where growth kinetics is finely perturbed by the input frequency and duty cycle. By the emulation of one's most preponderant mechanisms for brain's long-term memory, its implementation in vicinity of sensing arrays, neural probes or biochips shall greatly optimize computational costs and recognition required to classify high-dimensional patterns from complex environments.

Identifiants

pubmed: 34824266
doi: 10.1038/s41467-021-27274-9
pii: 10.1038/s41467-021-27274-9
pmc: PMC8617192
doi:

Banques de données

figshare
['10.6084/m9.figshare.16814710']

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

6898

Informations de copyright

© 2021. The Author(s).

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Auteurs

Kamila Janzakova (K)

Univ. Lille, CNRS, Centrale Lille, Univ. Polytechnique Hauts-de-France, UMR 8520-IEMN, 59000, Lille, France.

Ankush Kumar (A)

Univ. Lille, CNRS, Centrale Lille, Univ. Polytechnique Hauts-de-France, UMR 8520-IEMN, 59000, Lille, France.

Mahdi Ghazal (M)

Univ. Lille, CNRS, Centrale Lille, Univ. Polytechnique Hauts-de-France, UMR 8520-IEMN, 59000, Lille, France.

Anna Susloparova (A)

Univ. Lille, CNRS, Centrale Lille, Univ. Polytechnique Hauts-de-France, UMR 8520-IEMN, 59000, Lille, France.

Yannick Coffinier (Y)

Univ. Lille, CNRS, Centrale Lille, Univ. Polytechnique Hauts-de-France, UMR 8520-IEMN, 59000, Lille, France.

Fabien Alibart (F)

Univ. Lille, CNRS, Centrale Lille, Univ. Polytechnique Hauts-de-France, UMR 8520-IEMN, 59000, Lille, France.
Laboratoire Nanotechnologies & Nanosystèmes (LN2), CNRS, Université de Sherbrooke, Sherbrooke, QC, J1X0A5, Canada.

Sébastien Pecqueur (S)

Univ. Lille, CNRS, Centrale Lille, Univ. Polytechnique Hauts-de-France, UMR 8520-IEMN, 59000, Lille, France. sebastien.pecqueur@iemn.fr.

Classifications MeSH