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...
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| Published in | Frontiers in computational neuroscience Vol. 8; p. 36 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
Frontiers Research Foundation
04.04.2014
Frontiers Media S.A |
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| Online Access | Get full text |
| ISSN | 1662-5188 1662-5188 |
| DOI | 10.3389/fncom.2014.00036 |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| Author | Ritter, Petra Dinse, Hubert R. Roy, Dipanjan Sigala, Rodrigo Haufe, Sebastian |
| AuthorAffiliation | 4 Minerva Research Group BrainModes, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany 1 Department Neurology, Charité—University Medicine Berlin, Germany 2 Bernstein Focus State Dependencies of Learning, Bernstein Center for Computational Neuroscience Berlin, Germany 5 Berlin School of Mind and Brain, Mind and Brain Institute, Humboldt University Berlin, Germany 3 Neural Plasticity Lab, Institute for Neuroinformatics, Ruhr-University Bochum Bochum, Germany |
| AuthorAffiliation_xml | – name: 1 Department Neurology, Charité—University Medicine Berlin, Germany – name: 5 Berlin School of Mind and Brain, Mind and Brain Institute, Humboldt University Berlin, Germany – name: 3 Neural Plasticity Lab, Institute for Neuroinformatics, Ruhr-University Bochum Bochum, Germany – name: 2 Bernstein Focus State Dependencies of Learning, Bernstein Center for Computational Neuroscience Berlin, Germany – name: 4 Minerva Research Group BrainModes, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany |
| Author_xml | – sequence: 1 givenname: Rodrigo surname: Sigala fullname: Sigala, Rodrigo – sequence: 2 givenname: Sebastian surname: Haufe fullname: Haufe, Sebastian – sequence: 3 givenname: Dipanjan surname: Roy fullname: Roy, Dipanjan – sequence: 4 givenname: Hubert R. surname: Dinse fullname: Dinse, Hubert R. – sequence: 5 givenname: Petra surname: Ritter fullname: Ritter, Petra |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24772077$$D View this record in MEDLINE/PubMed |
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| Keywords | memory cognition alpha rhythm attention learning large-scale modeling oscillations |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 Reviewed by: Peter König, University of Osnabrück, Germany; Christian Bénar, Institut National de la Recherche Médicale, France Edited by: Viktor Jirsa, Aix-Marseille University, France This article was submitted to the journal Frontiers in Computational Neuroscience. |
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| Snippet | 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... 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... 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... |
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| SubjectTerms | Alpha Rhythm Attention Brain Cognition & reasoning Cognitive ability computational modeling Computational neuroscience Cortex Information processing Learning Memory Neural networks Neuromodulation Neuroscience Oscillations Perception spatial attention |
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| Title | The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models |
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