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Evolving conductive polymer neural networks on wetware

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Title: Evolving conductive polymer neural networks on wetware
Authors: Akai-Kasaya, Megumi Browse this author →KAKEN DB
Hagiwara, Naruki Browse this author
Hikita, Wataru Browse this author
Okada, Masaru Browse this author
Sugito, Yasumasa Browse this author
Kuwahara, Yuji Browse this author →KAKEN DB
Asai, Tetsuya Browse this author →KAKEN DB
Keywords: neural network
polymer wire
machine learning
Issue Date: 1-Jun-2020
Publisher: IOP Publishing
Journal Title: Japanese Journal of Applied Physics (JJAP)
Volume: 59
Issue: 6
Start Page: 060601
Publisher DOI: 10.35848/1347-4065/ab8e06
Abstract: Neural networks in the brain are structured in three-dimensional (3D) space, and the networks evolve through development and learning, whereas two-dimensional (2D) crossbars have essentially been optimized for a fully connected neural network, which results in a significant increase in unused memristors. Here, we present a prototype of molecular neural networks on wetware consisting of a space-free synaptic medium immersed in monomer solution. In the medium, conductive polymer wires are grown between multiple electrodes through learning only when necessary, i.e. no polymer wire is pre-placed, unlike present 2D crossbar devices. Through experiments, we found the necessary growth conditions for synaptic polymer wires. We first demonstrated the learning of simple Boolean functions and then data-encoding tasks by using our system comprising the synaptic media and their external controllers. These results are valuable for expanding the concept of space-free synapse development, i.e. extending our 2D synaptic media to 3D is possible in principle.
Rights: ©2020 The Japan Society of Applied Physics
Type: article (author version)
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 赤井 恵

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