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Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks
The classification of biological neuron types and networks poses challenges to the full understanding of the human brain’s organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal morphology and electrical types and their networks, based o...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573053/ https://www.ncbi.nlm.nih.gov/pubmed/36234792 http://dx.doi.org/10.3390/molecules27196256 |
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author | Barros, Michael Taynnan Siljak, Harun Mullen, Peter Papadias, Constantinos Hyttinen, Jari Marchetti, Nicola |
author_facet | Barros, Michael Taynnan Siljak, Harun Mullen, Peter Papadias, Constantinos Hyttinen, Jari Marchetti, Nicola |
author_sort | Barros, Michael Taynnan |
collection | PubMed |
description | The classification of biological neuron types and networks poses challenges to the full understanding of the human brain’s organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal morphology and electrical types and their networks, based on the attributes of neuronal communication using supervised machine learning solutions. This presents advantages compared to the existing approaches in neuroinformatics since the data related to mutual information or delay between neurons obtained from spike trains are more abundant than conventional morphological data. We constructed two open-access computational platforms of various neuronal circuits from the Blue Brain Project realistic models, named Neurpy and Neurgen. Then, we investigated how we could perform network tomography with cortical neuronal circuits for the morphological, topological and electrical classification of neurons. We extracted the simulated data of 10,000 network topology combinations with five layers, 25 morphological type (m-type) cells, and 14 electrical type (e-type) cells. We applied the data to several different classifiers (including Support Vector Machine (SVM), Decision Trees, Random Forest, and Artificial Neural Networks). We achieved accuracies of up to 70%, and the inference of biological network structures using network tomography reached up to 65% of accuracy. Objective classification of biological networks can be achieved with cascaded machine learning methods using neuron communication data. SVM methods seem to perform better amongst used techniques. Our research not only contributes to existing classification efforts but sets the road-map for future usage of brain–machine interfaces towards an in vivo objective classification of neurons as a sensing mechanism of the brain’s structure. |
format | Online Article Text |
id | pubmed-9573053 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95730532022-10-17 Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks Barros, Michael Taynnan Siljak, Harun Mullen, Peter Papadias, Constantinos Hyttinen, Jari Marchetti, Nicola Molecules Article The classification of biological neuron types and networks poses challenges to the full understanding of the human brain’s organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal morphology and electrical types and their networks, based on the attributes of neuronal communication using supervised machine learning solutions. This presents advantages compared to the existing approaches in neuroinformatics since the data related to mutual information or delay between neurons obtained from spike trains are more abundant than conventional morphological data. We constructed two open-access computational platforms of various neuronal circuits from the Blue Brain Project realistic models, named Neurpy and Neurgen. Then, we investigated how we could perform network tomography with cortical neuronal circuits for the morphological, topological and electrical classification of neurons. We extracted the simulated data of 10,000 network topology combinations with five layers, 25 morphological type (m-type) cells, and 14 electrical type (e-type) cells. We applied the data to several different classifiers (including Support Vector Machine (SVM), Decision Trees, Random Forest, and Artificial Neural Networks). We achieved accuracies of up to 70%, and the inference of biological network structures using network tomography reached up to 65% of accuracy. Objective classification of biological networks can be achieved with cascaded machine learning methods using neuron communication data. SVM methods seem to perform better amongst used techniques. Our research not only contributes to existing classification efforts but sets the road-map for future usage of brain–machine interfaces towards an in vivo objective classification of neurons as a sensing mechanism of the brain’s structure. MDPI 2022-09-23 /pmc/articles/PMC9573053/ /pubmed/36234792 http://dx.doi.org/10.3390/molecules27196256 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Barros, Michael Taynnan Siljak, Harun Mullen, Peter Papadias, Constantinos Hyttinen, Jari Marchetti, Nicola Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks |
title | Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks |
title_full | Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks |
title_fullStr | Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks |
title_full_unstemmed | Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks |
title_short | Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks |
title_sort | objective supervised machine learning-based classification and inference of biological neuronal networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573053/ https://www.ncbi.nlm.nih.gov/pubmed/36234792 http://dx.doi.org/10.3390/molecules27196256 |
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