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The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models
During the past two decades growing evidence indicates that brain oscillations in the alpha band (~10 Hz) not only reflect an “idle” state of cortical activity, but also take a more active role in the generation of complex cognitive functions. A recent study shows that more than 60% of the observed...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3983484/ https://www.ncbi.nlm.nih.gov/pubmed/24772077 http://dx.doi.org/10.3389/fncom.2014.00036 |
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author | Sigala, Rodrigo Haufe, Sebastian Roy, Dipanjan Dinse, Hubert R. Ritter, Petra |
author_facet | Sigala, Rodrigo Haufe, Sebastian Roy, Dipanjan Dinse, Hubert R. Ritter, Petra |
author_sort | Sigala, Rodrigo |
collection | PubMed |
description | During the past two decades growing evidence indicates that brain oscillations in the alpha band (~10 Hz) not only reflect an “idle” state of cortical activity, but also take a more active role in the generation of complex cognitive functions. A recent study shows that more than 60% of the observed inter-subject variability in perceptual learning can be ascribed to ongoing alpha activity. This evidence indicates a significant role of alpha oscillations for perceptual learning and hence motivates to explore the potential underlying mechanisms. Hence, it is the purpose of this review to highlight existent evidence that ascribes intrinsic alpha oscillations a role in shaping our ability to learn. In the review, we disentangle the alpha rhythm into different neural signatures that control information processing within individual functional building blocks of perceptual learning. We further highlight computational studies that shed light on potential mechanisms regarding how alpha oscillations may modulate information transfer and connectivity changes relevant for learning. To enable testing of those model based hypotheses, we emphasize the need for multidisciplinary approaches combining assessment of behavior and multi-scale neuronal activity, active modulation of ongoing brain states and computational modeling to reveal the mathematical principles of the complex neuronal interactions. In particular we highlight the relevance of multi-scale modeling frameworks such as the one currently being developed by “The Virtual Brain” project. |
format | Online Article Text |
id | pubmed-3983484 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-39834842014-04-25 The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models Sigala, Rodrigo Haufe, Sebastian Roy, Dipanjan Dinse, Hubert R. Ritter, Petra Front Comput Neurosci Neuroscience During the past two decades growing evidence indicates that brain oscillations in the alpha band (~10 Hz) not only reflect an “idle” state of cortical activity, but also take a more active role in the generation of complex cognitive functions. A recent study shows that more than 60% of the observed inter-subject variability in perceptual learning can be ascribed to ongoing alpha activity. This evidence indicates a significant role of alpha oscillations for perceptual learning and hence motivates to explore the potential underlying mechanisms. Hence, it is the purpose of this review to highlight existent evidence that ascribes intrinsic alpha oscillations a role in shaping our ability to learn. In the review, we disentangle the alpha rhythm into different neural signatures that control information processing within individual functional building blocks of perceptual learning. We further highlight computational studies that shed light on potential mechanisms regarding how alpha oscillations may modulate information transfer and connectivity changes relevant for learning. To enable testing of those model based hypotheses, we emphasize the need for multidisciplinary approaches combining assessment of behavior and multi-scale neuronal activity, active modulation of ongoing brain states and computational modeling to reveal the mathematical principles of the complex neuronal interactions. In particular we highlight the relevance of multi-scale modeling frameworks such as the one currently being developed by “The Virtual Brain” project. Frontiers Media S.A. 2014-04-04 /pmc/articles/PMC3983484/ /pubmed/24772077 http://dx.doi.org/10.3389/fncom.2014.00036 Text en Copyright © 2014 Sigala, Haufe, Roy, Dinse and Ritter. http://creativecommons.org/licenses/by/3.0/ 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) or licensor 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 Sigala, Rodrigo Haufe, Sebastian Roy, Dipanjan Dinse, Hubert R. Ritter, Petra The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models |
title | The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models |
title_full | The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models |
title_fullStr | The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models |
title_full_unstemmed | The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models |
title_short | The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models |
title_sort | role of alpha-rhythm states in perceptual learning: insights from experiments and computational models |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3983484/ https://www.ncbi.nlm.nih.gov/pubmed/24772077 http://dx.doi.org/10.3389/fncom.2014.00036 |
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