Neuronal assembly detection and cell membership specification by principal component analysis
In 1949, Donald Hebb postulated that assemblies of synchronously activated neurons are the elementary units of information processing in the brain. Despite being one of the most influential theories in neuroscience, Hebb's cell assembly hypothesis only started to become testable in the past two...
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| Veröffentlicht in: | PloS one 2011-06, Vol.6 (6), p.e20996-e20996 |
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| creator | Lopes-dos-Santos, Vítor Conde-Ocazionez, Sergio Nicolelis, Miguel A L Ribeiro, Sidarta T Tort, Adriano B L |
| description | In 1949, Donald Hebb postulated that assemblies of synchronously activated neurons are the elementary units of information processing in the brain. Despite being one of the most influential theories in neuroscience, Hebb's cell assembly hypothesis only started to become testable in the past two decades due to technological advances. However, while the technology for the simultaneous recording of large neuronal populations undergoes fast development, there is still a paucity of analytical methods that can properly detect and track the activity of cell assemblies. Here we describe a principal component-based method that is able to (1) identify all cell assemblies present in the neuronal population investigated, (2) determine the number of neurons involved in ensemble activity, (3) specify the precise identity of the neurons pertaining to each cell assembly, and (4) unravel the time course of the individual activity of multiple assemblies. Application of the method to multielectrode recordings of awake and behaving rats revealed that assemblies detected in the cerebral cortex and hippocampus typically contain overlapping neurons. The results indicate that the PCA method presented here is able to properly detect, track and specify neuronal assemblies, irrespective of overlapping membership. |
| doi_str_mv | 10.1371/journal.pone.0020996 |
| format | Article |
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Despite being one of the most influential theories in neuroscience, Hebb's cell assembly hypothesis only started to become testable in the past two decades due to technological advances. However, while the technology for the simultaneous recording of large neuronal populations undergoes fast development, there is still a paucity of analytical methods that can properly detect and track the activity of cell assemblies. Here we describe a principal component-based method that is able to (1) identify all cell assemblies present in the neuronal population investigated, (2) determine the number of neurons involved in ensemble activity, (3) specify the precise identity of the neurons pertaining to each cell assembly, and (4) unravel the time course of the individual activity of multiple assemblies. Application of the method to multielectrode recordings of awake and behaving rats revealed that assemblies detected in the cerebral cortex and hippocampus typically contain overlapping neurons. The results indicate that the PCA method presented here is able to properly detect, track and specify neuronal assemblies, irrespective of overlapping membership.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0020996</identifier><identifier>PMID: 21698248</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Analysis ; Analytical methods ; Assemblies ; Assembly ; Biology ; Brain ; Cerebral cortex ; Data analysis ; Data processing ; Information processing ; Nervous system ; Neurons ; Neurons - cytology ; Neurosciences ; Principal Component Analysis ; Principal components analysis ; Rats ; Santos, Sergio ; Technology application ; Trends</subject><ispartof>PloS one, 2011-06, Vol.6 (6), p.e20996-e20996</ispartof><rights>COPYRIGHT 2011 Public Library of Science</rights><rights>2011 Lopes-dos-Santos et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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| subjects | Algorithms Analysis Analytical methods Assemblies Assembly Biology Brain Cerebral cortex Data analysis Data processing Information processing Nervous system Neurons Neurons - cytology Neurosciences Principal Component Analysis Principal components analysis Rats Santos, Sergio Technology application Trends |
| title | Neuronal assembly detection and cell membership specification by principal component analysis |
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