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A Mathematical Theory of Cortex-Receptor Artificial Extension

Many physiology experiments demonstrate that an organism’s cortex and receptor system can be artificially extended, giving the organism new types of perceptual capabilities. To examine artificial extension of the cortex-receptor system, I propose a computational model that allows new types of sensor...

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Autor principal: Xing, You-Lu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6972759/
https://www.ncbi.nlm.nih.gov/pubmed/31964907
http://dx.doi.org/10.1038/s41598-020-57591-w
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author Xing, You-Lu
author_facet Xing, You-Lu
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description Many physiology experiments demonstrate that an organism’s cortex and receptor system can be artificially extended, giving the organism new types of perceptual capabilities. To examine artificial extension of the cortex-receptor system, I propose a computational model that allows new types of sensory pathways to be added directly to the computational model itself in an online manner. A synapse expandable artificial neuron model that can grow new synapses, forming a bridge between the novel perceptual information and the existing neural network is introduced to absorb the novel sensory pathway. The experimental results show that the computational model can effectively integrate sudden emerged sensory channels and the neural circuits in the computational model can be reused for novel modalities without influencing the original modality.
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spelling pubmed-69727592020-01-27 A Mathematical Theory of Cortex-Receptor Artificial Extension Xing, You-Lu Sci Rep Article Many physiology experiments demonstrate that an organism’s cortex and receptor system can be artificially extended, giving the organism new types of perceptual capabilities. To examine artificial extension of the cortex-receptor system, I propose a computational model that allows new types of sensory pathways to be added directly to the computational model itself in an online manner. A synapse expandable artificial neuron model that can grow new synapses, forming a bridge between the novel perceptual information and the existing neural network is introduced to absorb the novel sensory pathway. The experimental results show that the computational model can effectively integrate sudden emerged sensory channels and the neural circuits in the computational model can be reused for novel modalities without influencing the original modality. Nature Publishing Group UK 2020-01-21 /pmc/articles/PMC6972759/ /pubmed/31964907 http://dx.doi.org/10.1038/s41598-020-57591-w Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Xing, You-Lu
A Mathematical Theory of Cortex-Receptor Artificial Extension
title A Mathematical Theory of Cortex-Receptor Artificial Extension
title_full A Mathematical Theory of Cortex-Receptor Artificial Extension
title_fullStr A Mathematical Theory of Cortex-Receptor Artificial Extension
title_full_unstemmed A Mathematical Theory of Cortex-Receptor Artificial Extension
title_short A Mathematical Theory of Cortex-Receptor Artificial Extension
title_sort mathematical theory of cortex-receptor artificial extension
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6972759/
https://www.ncbi.nlm.nih.gov/pubmed/31964907
http://dx.doi.org/10.1038/s41598-020-57591-w
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