1. Analog programing of conducting-polymer dendritic interconnections and control of their morphology
- Author
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Fabien Alibart, Anna Susloparova, Sébastien Pecqueur, Kamila Janzakova, Mahdi Ghazal, Yannick Coffinier, Ankush Kumar, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), Nanostructures, nanoComponents & Molecules - IEMN (NCM - IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), NanoBioInterfaces - IEMN (NBI - IEMN), Laboratoire Nanotechnologies et Nanosystèmes [Sherbrooke] (LN2), Université de Sherbrooke (UdeS)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-École Supérieure de Chimie Physique Électronique de Lyon (CPE)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), European Commission: H2020-EU.1.1.ERC project IONOS (# GA 773228-recipient: F.A.)., Renatech Network, European Project: 773228,H2020,ERC-2017-COG,IONOS(2018), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), and Université de Lyon-Université de Lyon-École supérieure de Chimie Physique Electronique de Lyon (CPE)-Institut National des Sciences Appliquées de Lyon (INSA Lyon)
- Subjects
Bridging (networking) ,Computer science ,Science ,General Physics and Astronomy ,FOS: Physical sciences ,Image processing ,02 engineering and technology ,Applied Physics (physics.app-ph) ,010402 general chemistry ,01 natural sciences ,General Biochemistry, Genetics and Molecular Biology ,Article ,Electrochemistry ,Electronic devices ,Electronics ,[PHYS.COND.CM-DS-NN]Physics [physics]/Condensed Matter [cond-mat]/Disordered Systems and Neural Networks [cond-mat.dis-nn] ,Biochip ,Emulation ,Multidisciplinary ,Process (computing) ,General Chemistry ,Physics - Applied Physics ,Disordered Systems and Neural Networks (cond-mat.dis-nn) ,[CHIM.MATE]Chemical Sciences/Material chemistry ,Condensed Matter - Disordered Systems and Neural Networks ,021001 nanoscience & nanotechnology ,Electrical and electronic engineering ,0104 chemical sciences ,[SPI.TRON]Engineering Sciences [physics]/Electronics ,Neuromorphic engineering ,Duty cycle ,0210 nano-technology ,Biological system - Abstract
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., Despite advances in brain-inspired computing, existing electronics use top-down processes that do not compare with neural connections in the brain. Here, the authors report an electrically-tunable electropolymerization process that emulates and controls neural dendritic morphogenesis.
- Published
- 2021