Artificial Astrocytes Improve Neural Network Performance
Publication Date
April 19, 2011
Journal
PLOS ONE
Authors
Ana B. Porto Pazos, Noha Veiguela, Pablo Mesejo, Marta Navarrete, et al
Volume
6
Issue
4
Pages
e19109
DOI
https://dx.plos.org/10.1371/journal.pone.0019109
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0019109
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/21526157
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3079756
Europe PMC
http://europepmc.org/abstract/MED/21526157
Web of Science
000289671100052
Scopus
79955445385
Mendeley
http://www.mendeley.com/research/artificial-astrocytes-improve-neural-network-performance
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Mendeley | Further Information

{"title"=>"Artificial astrocytes improve neural network performance", "type"=>"journal", "authors"=>[{"first_name"=>"Ana B.", "last_name"=>"Porto-Pazos", "scopus_author_id"=>"34571759000"}, {"first_name"=>"Noha", "last_name"=>"Veiguela", "scopus_author_id"=>"37108711700"}, {"first_name"=>"Pablo", "last_name"=>"Mesejo", "scopus_author_id"=>"37108185800"}, {"first_name"=>"Marta", "last_name"=>"Navarrete", "scopus_author_id"=>"23978630700"}, {"first_name"=>"Alberto", "last_name"=>"Alvarellos", "scopus_author_id"=>"37661031200"}, {"first_name"=>"Oscar", "last_name"=>"Ibáñez", "scopus_author_id"=>"14833915000"}, {"first_name"=>"Alejandro", "last_name"=>"Pazos", "scopus_author_id"=>"7006526793"}, {"first_name"=>"Alfonso", "last_name"=>"Araque", "scopus_author_id"=>"7004288424"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"361673269", "issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "doi"=>"10.1371/journal.pone.0019109", "scopus"=>"2-s2.0-79955445385", "pmid"=>"21526157", "sgr"=>"79955445385"}, "id"=>"a6d01397-fce6-3cfe-b9af-1d4176778dd0", "abstract"=>"Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.", "link"=>"http://www.mendeley.com/research/artificial-astrocytes-improve-neural-network-performance", "reader_count"=>107, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>7, "Librarian"=>1, "Student > Doctoral Student"=>6, "Researcher"=>10, "Student > Ph. D. Student"=>25, "Student > Postgraduate"=>8, "Student > Master"=>18, "Other"=>5, "Student > Bachelor"=>15, "Lecturer"=>3, "Professor"=>7}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>7, "Librarian"=>1, "Student > Doctoral Student"=>6, "Researcher"=>10, "Student > Ph. D. Student"=>25, "Student > Postgraduate"=>8, "Student > Master"=>18, "Other"=>5, "Student > Bachelor"=>15, "Lecturer"=>3, "Professor"=>7}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Agricultural and Biological Sciences"=>28, "Arts and Humanities"=>1, "Chemistry"=>1, "Computer Science"=>32, "Earth and Planetary Sciences"=>2, "Engineering"=>15, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>2, "Medicine and Dentistry"=>6, "Neuroscience"=>5, "Physics and Astronomy"=>6, "Psychology"=>4, "Social Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>6}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>6}, "Psychology"=>{"Psychology"=>4}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>3}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Engineering"=>{"Engineering"=>15}, "Chemistry"=>{"Chemistry"=>1}, "Neuroscience"=>{"Neuroscience"=>5}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>28}, "Computer Science"=>{"Computer Science"=>32}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}}, "reader_count_by_country"=>{"Sweden"=>1, "Belgium"=>1, "Iran"=>1, "United States"=>2, "Ireland"=>1, "Brazil"=>1, "United Kingdom"=>2, "Slovakia"=>2, "Chile"=>1, "France"=>2, "Germany"=>1}, "group_count"=>7}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/782335"], "description"=>"<p>(<b>A</b>) Schematic drawing representing the design of artificial neural networks without (left) and with artificial astrocytes (red stars; right) designed to solve the Ionosphere (IS) problem. (<b>B</b>) Representative example (left) and mean training accuracy (n = 100) vs. time for the (NN) and (NGN) solving the IS problem. (<b>C</b>) Representative example (left) and mean test accuracy (n = 100) vs. time for the NN and NGN solving the IS problem. (<b>D</b>) Mean steady training and test accuracies (left and right, respectively; n = 100) of NN and NGN solving the four problems tested. (<b>E</b>) Mean training and test times (left and right, respectively; n = 100) of NN and NGN solving the four problems tested. *P<0.05, **P<0.01 and ***P<0.001. Values represent mean ± S.E.M.</p>", "links"=>[], "tags"=>["astrocytes", "neural"], "article_id"=>452689, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Ana B. Porto-Pazos", "Noha Veiguela", "Pablo Mesejo", "Marta Navarrete", "Alberto Alvarellos", "Oscar Ibáñez", "Alejandro Pazos", "Alfonso Araque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019109.g001", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Artificial_astrocytes_enhance_neural_network_performance_/452689", "title"=>"Artificial astrocytes enhance neural network performance.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 19:05:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/782462"], "description"=>"<p>(<b>A</b>) Schematic drawing representing the design of three artificial neural networks with different number of neurons and different architectures. (<b>B and C</b>) Mean steady training and test accuracies, respectively (n = 100) of each NN for each problem tested. (<b>D</b>) Mean training and test times (left and right, respectively; n = 100) of each NN for each problem tested. *P<0.05, **P<0.01 and ***P<0.001. Values represent mean ± S.E.M.</p>", "links"=>[], "tags"=>["neurons"], "article_id"=>452828, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Ana B. Porto-Pazos", "Noha Veiguela", "Pablo Mesejo", "Marta Navarrete", "Alberto Alvarellos", "Oscar Ibáñez", "Alejandro Pazos", "Alfonso Araque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019109.g002", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Neural_network_performance_does_not_depend_on_the_number_of_neurons_or_the_architecture_of_the_network_/452828", "title"=>"Neural network performance does not depend on the number of neurons or the architecture of the network.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 19:05:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/782537"], "description"=>"<p>(<b>A</b>) Mean steady training and test accuracies (left and right, respectively; n = 100) of NN and NGN with 1, 2 or 3 hidden layers to solve the four problems tested. (<b>B</b>) Performance indexes (i.e., NGN values relative to NN values) of the training and test accuracies (left and right, respectively). Red symbols represent the corresponding averaged values (n = 16). *P<0.05, **P<0.01 and ***P<0.001. Values represent mean ± S.E.M.</p>", "links"=>[], "tags"=>["astrocytes", "increases"], "article_id"=>452906, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Ana B. Porto-Pazos", "Noha Veiguela", "Pablo Mesejo", "Marta Navarrete", "Alberto Alvarellos", "Oscar Ibáñez", "Alejandro Pazos", "Alfonso Araque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019109.g003", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Network_performance_improvement_by_artificial_astrocytes_increases_as_the_network_complexity_increases_/452906", "title"=>"Network performance improvement by artificial astrocytes increases as the network complexity increases.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 19:06:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/782722"], "description"=>"<p>Architectures of NN used in each problem.</p>", "links"=>[], "tags"=>["nn"], "article_id"=>453093, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Ana B. Porto-Pazos", "Noha Veiguela", "Pablo Mesejo", "Marta Navarrete", "Alberto Alvarellos", "Oscar Ibáñez", "Alejandro Pazos", "Alfonso Araque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019109.t001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Architectures_of_NN_used_in_each_problem_/453093", "title"=>"Architectures of NN used in each problem.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 19:07:17"}
  • {"files"=>["https://ndownloader.figshare.com/files/782686"], "description"=>"<p>Stop times during the training phase (minutes).</p>", "links"=>[], "tags"=>["times"], "article_id"=>453061, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Ana B. Porto-Pazos", "Noha Veiguela", "Pablo Mesejo", "Marta Navarrete", "Alberto Alvarellos", "Oscar Ibáñez", "Alejandro Pazos", "Alfonso Araque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019109.t002", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Stop_times_during_the_training_phase_minutes_/453061", "title"=>"Stop times during the training phase (minutes).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 19:07:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/782610"], "description"=>"<p>(<b>A</b>) Mean steady training and test accuracies (left and right, respectively; n = 100) of NN and NGN with 1, 2 or 3 hidden layers to solve the four problems tested. (<b>B</b>) Performance indexes (i.e., NGN values relative to NN values) of the training and test accuracies (left and right, respectively). Red symbols represent the corresponding averaged values (n = 12). (<b>C</b>) Mean performance indexes of the training and test accuracies (left and right, respectively; n = 100) for each problem tested when artificial astrocytes were stimulated by different patterns of neuronal connection activity. The notation n,m indicates that artificial astrocytes were stimulated when the associated neuronal connections were active for at least n out of m iterations. (<b>D</b>) Mean performance indexes of the training and test accuracies (left and right, respectively; n = 100) for each problem of NGN with non-selected (black bars) or with specifically selected neuron-glia interaction parameters (red bars). *P<0.05, **P<0.01 and ***P<0.001. Values represent mean ± S.E.M.</p>", "links"=>[], "tags"=>["astrocytes", "depends"], "article_id"=>452982, "categories"=>["Information And Computing Sciences", "Neuroscience"], "users"=>["Ana B. Porto-Pazos", "Noha Veiguela", "Pablo Mesejo", "Marta Navarrete", "Alberto Alvarellos", "Oscar Ibáñez", "Alejandro Pazos", "Alfonso Araque"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019109.g004", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relative_network_performance_improvement_by_artificial_astrocytes_depends_on_the_problem_tested_/452982", "title"=>"Relative network performance improvement by artificial astrocytes depends on the problem tested.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 19:06:40"}

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Relative Metric

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