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Bio-Inspired Evolutionary Model of Spiking Neural Networks in Ionic Liquid Space
One of the biggest struggles while working with artificial neural networks is being able to come up with models which closely match biological observations. Biological neural networks seem to capable of creating and pruning dendritic spines, leading to synapses being changed, which results in higher...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6856051/ https://www.ncbi.nlm.nih.gov/pubmed/31787863 http://dx.doi.org/10.3389/fnins.2019.01085 |
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author | Iranmehr, Ensieh Shouraki, Saeed Bagheri Faraji, Mohammad Mahdi Bagheri, Nasim Linares-Barranco, Bernabe |
author_facet | Iranmehr, Ensieh Shouraki, Saeed Bagheri Faraji, Mohammad Mahdi Bagheri, Nasim Linares-Barranco, Bernabe |
author_sort | Iranmehr, Ensieh |
collection | PubMed |
description | One of the biggest struggles while working with artificial neural networks is being able to come up with models which closely match biological observations. Biological neural networks seem to capable of creating and pruning dendritic spines, leading to synapses being changed, which results in higher learning capability. The latter forms the basis of the present study in which a new ionic model for reservoir-like networks, consisting of spiking neurons, is introduced. High plasticity of this model makes learning possible with a fewer number of neurons. In order to study the effect of the applied stimulus in an ionic liquid space through time, a diffusion operator is used which somehow compensates for the separation between spatial and temporal coding in spiking neural networks and therefore, makes the mentioned model suitable for spatiotemporal patterns. Inspired by partial structural changes in the human brain over the years, the proposed model evolves during the learning process. The effect of topological evolution on the proposed model's performance for some classification problems is studied in this paper. Several datasets have been used to evaluate the performance of the proposed model compared to the original LSM. Classification results via separation and accuracy values have shown that the proposed ionic liquid outperforms the original LSM. |
format | Online Article Text |
id | pubmed-6856051 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-68560512019-11-29 Bio-Inspired Evolutionary Model of Spiking Neural Networks in Ionic Liquid Space Iranmehr, Ensieh Shouraki, Saeed Bagheri Faraji, Mohammad Mahdi Bagheri, Nasim Linares-Barranco, Bernabe Front Neurosci Neuroscience One of the biggest struggles while working with artificial neural networks is being able to come up with models which closely match biological observations. Biological neural networks seem to capable of creating and pruning dendritic spines, leading to synapses being changed, which results in higher learning capability. The latter forms the basis of the present study in which a new ionic model for reservoir-like networks, consisting of spiking neurons, is introduced. High plasticity of this model makes learning possible with a fewer number of neurons. In order to study the effect of the applied stimulus in an ionic liquid space through time, a diffusion operator is used which somehow compensates for the separation between spatial and temporal coding in spiking neural networks and therefore, makes the mentioned model suitable for spatiotemporal patterns. Inspired by partial structural changes in the human brain over the years, the proposed model evolves during the learning process. The effect of topological evolution on the proposed model's performance for some classification problems is studied in this paper. Several datasets have been used to evaluate the performance of the proposed model compared to the original LSM. Classification results via separation and accuracy values have shown that the proposed ionic liquid outperforms the original LSM. Frontiers Media S.A. 2019-11-08 /pmc/articles/PMC6856051/ /pubmed/31787863 http://dx.doi.org/10.3389/fnins.2019.01085 Text en Copyright © 2019 Iranmehr, Shouraki, Faraji, Bagheri and Linares-Barranco. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Iranmehr, Ensieh Shouraki, Saeed Bagheri Faraji, Mohammad Mahdi Bagheri, Nasim Linares-Barranco, Bernabe Bio-Inspired Evolutionary Model of Spiking Neural Networks in Ionic Liquid Space |
title | Bio-Inspired Evolutionary Model of Spiking Neural Networks in Ionic Liquid Space |
title_full | Bio-Inspired Evolutionary Model of Spiking Neural Networks in Ionic Liquid Space |
title_fullStr | Bio-Inspired Evolutionary Model of Spiking Neural Networks in Ionic Liquid Space |
title_full_unstemmed | Bio-Inspired Evolutionary Model of Spiking Neural Networks in Ionic Liquid Space |
title_short | Bio-Inspired Evolutionary Model of Spiking Neural Networks in Ionic Liquid Space |
title_sort | bio-inspired evolutionary model of spiking neural networks in ionic liquid space |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6856051/ https://www.ncbi.nlm.nih.gov/pubmed/31787863 http://dx.doi.org/10.3389/fnins.2019.01085 |
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