Cargando…

A functional assembly framework based on implementable neurobionic material

Neurobionic material is an emerging field in material and translational science. For material design, much focus has already been transferred from von Neumann architecture to the neuromorphic framework. As it is impractical to reconstruct the real neural tissue solely from materials, it is necessary...

Descripción completa

Detalles Bibliográficos
Autores principales: Zou, Xiang, Jiang, Conglin, Sun, Yirui, Zhao, Donghua, Tong, Yusheng, Mao, Ying, Chen, Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805406/
https://www.ncbi.nlm.nih.gov/pubmed/33463062
http://dx.doi.org/10.1002/ctm2.277
_version_ 1783636304571924480
author Zou, Xiang
Jiang, Conglin
Sun, Yirui
Zhao, Donghua
Tong, Yusheng
Mao, Ying
Chen, Liang
author_facet Zou, Xiang
Jiang, Conglin
Sun, Yirui
Zhao, Donghua
Tong, Yusheng
Mao, Ying
Chen, Liang
author_sort Zou, Xiang
collection PubMed
description Neurobionic material is an emerging field in material and translational science. For material design, much focus has already been transferred from von Neumann architecture to the neuromorphic framework. As it is impractical to reconstruct the real neural tissue solely from materials, it is necessary to develop a feasible neurobionics framework to realize advanced brain function. In this study, we proposed a mathematical neurobionic material model, and attempted to explore advanced function only by simple and feasible structures. Here an equivalent simplified framework was used to describe the dynamics expressed in an equation set, while in vivo study was performed to verify simulation results. In neural tissue, the output of neurobionic material was characterized by spike frequency, and the stability is based on the excitatory/inhibitory proportion. Spike frequency in mathematical neurobionic material model can spontaneously meet the solution of a nonlinear equation set. Assembly can also evolve into a certain distribution under different stimulations, closely related to decision making. Short‐term memory can be formed by coupling neurobionic material assemblies. In vivo experiments further confirmed predictions in our mathematical neurobionic material model. The property of this neural biomimetic material model demonstrates its intrinsic neuromorphic computational ability, which should offer promises for implementable neurobionic device design.
format Online
Article
Text
id pubmed-7805406
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-78054062021-01-22 A functional assembly framework based on implementable neurobionic material Zou, Xiang Jiang, Conglin Sun, Yirui Zhao, Donghua Tong, Yusheng Mao, Ying Chen, Liang Clin Transl Med Research Articles Neurobionic material is an emerging field in material and translational science. For material design, much focus has already been transferred from von Neumann architecture to the neuromorphic framework. As it is impractical to reconstruct the real neural tissue solely from materials, it is necessary to develop a feasible neurobionics framework to realize advanced brain function. In this study, we proposed a mathematical neurobionic material model, and attempted to explore advanced function only by simple and feasible structures. Here an equivalent simplified framework was used to describe the dynamics expressed in an equation set, while in vivo study was performed to verify simulation results. In neural tissue, the output of neurobionic material was characterized by spike frequency, and the stability is based on the excitatory/inhibitory proportion. Spike frequency in mathematical neurobionic material model can spontaneously meet the solution of a nonlinear equation set. Assembly can also evolve into a certain distribution under different stimulations, closely related to decision making. Short‐term memory can be formed by coupling neurobionic material assemblies. In vivo experiments further confirmed predictions in our mathematical neurobionic material model. The property of this neural biomimetic material model demonstrates its intrinsic neuromorphic computational ability, which should offer promises for implementable neurobionic device design. John Wiley and Sons Inc. 2021-01-13 /pmc/articles/PMC7805406/ /pubmed/33463062 http://dx.doi.org/10.1002/ctm2.277 Text en © 2021 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Zou, Xiang
Jiang, Conglin
Sun, Yirui
Zhao, Donghua
Tong, Yusheng
Mao, Ying
Chen, Liang
A functional assembly framework based on implementable neurobionic material
title A functional assembly framework based on implementable neurobionic material
title_full A functional assembly framework based on implementable neurobionic material
title_fullStr A functional assembly framework based on implementable neurobionic material
title_full_unstemmed A functional assembly framework based on implementable neurobionic material
title_short A functional assembly framework based on implementable neurobionic material
title_sort functional assembly framework based on implementable neurobionic material
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805406/
https://www.ncbi.nlm.nih.gov/pubmed/33463062
http://dx.doi.org/10.1002/ctm2.277
work_keys_str_mv AT zouxiang afunctionalassemblyframeworkbasedonimplementableneurobionicmaterial
AT jiangconglin afunctionalassemblyframeworkbasedonimplementableneurobionicmaterial
AT sunyirui afunctionalassemblyframeworkbasedonimplementableneurobionicmaterial
AT zhaodonghua afunctionalassemblyframeworkbasedonimplementableneurobionicmaterial
AT tongyusheng afunctionalassemblyframeworkbasedonimplementableneurobionicmaterial
AT maoying afunctionalassemblyframeworkbasedonimplementableneurobionicmaterial
AT chenliang afunctionalassemblyframeworkbasedonimplementableneurobionicmaterial
AT zouxiang functionalassemblyframeworkbasedonimplementableneurobionicmaterial
AT jiangconglin functionalassemblyframeworkbasedonimplementableneurobionicmaterial
AT sunyirui functionalassemblyframeworkbasedonimplementableneurobionicmaterial
AT zhaodonghua functionalassemblyframeworkbasedonimplementableneurobionicmaterial
AT tongyusheng functionalassemblyframeworkbasedonimplementableneurobionicmaterial
AT maoying functionalassemblyframeworkbasedonimplementableneurobionicmaterial
AT chenliang functionalassemblyframeworkbasedonimplementableneurobionicmaterial