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  State-dependencies of learning across brain scales

Ritter, P., Born, J., Brecht, M., Dinse, H., Heinemann, U., Pleger, B., et al. (2015). State-dependencies of learning across brain scales. Frontiers in Computational Neuroscience, 9: 1. doi:10.3389/fncom.2015.00001.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0024-6BF3-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-7892-1
Genre: Journal Article

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 Creators:
Ritter, Petra1, 2, 3, 4, Author              
Born, Jan5, Author
Brecht, Michael3, Author
Dinse, Hubert6, 7, Author
Heinemann, Uwe3, 8, Author
Pleger, Burkhard9, 10, Author              
Schmitz, Dietmar3, 8, 11, 12, 13, Author
Schreiber, Susanne3, 14, Author
Villringer, Arno4, 9, 10, Author              
Kempter, Richard3, 14, Author
Affiliations:
1Minerva Research Group Brain Modes, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_751546              
2Department of Neurology, Charité University Medicine Berlin, Germany, ou_persistent22              
3Bernstein Center for Computational Neuroscience, Berlin, Germany, ou_persistent22              
4Berlin School of Mind and Brain, Humboldt University Berlin, Germany, ou_persistent22              
5Institute of Medical Psychology and Behavioral Neurobiology, Eberhard Karls University Tübingen, Germany, ou_persistent22              
6Neural Plasticity Lab, Institute for Neuroinformatics, Ruhr University, Bochum, Germany, ou_persistent22              
7Department of Neurology, University Hospital Bergmannsheil, Bochum, Germany, ou_persistent22              
8NeuroCure Cluster of Excellence, Charité University Medicine Berlin, Germany, ou_persistent22              
9Clinic for Cognitive Neurology, University of Leipzig, Germany, ou_persistent22              
10Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_634549              
11Neuroscience Research Center NWFZ, Charité University Medicine Berlin, Germany, ou_persistent22              
12Max Delbrück Center for Molecular Medicine, Berlin, Germany, ou_persistent22              
13German Center for Neurodegenerative Diseases, Berlin, Germany, ou_persistent22              
14Department of Biology, Institute for Theoretical Biology, Humboldt University Berlin, Germany, ou_persistent22              

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Free keywords: Learning; Plasticity; Brain scales; State-dependency; Computational modeling
 Abstract: Learning is a complex brain function operating on different time scales, from milliseconds to years, which induces enduring changes in brain dynamics. The brain also undergoes continuous ‘spontaneous’ shifts in states, which, amongst others, are characterized by rhythmic activity of various frequencies. Besides the most obvious distinct modes of waking and sleep, wake-associated brain states comprise modulations of vigilance and attention. Recent findings show that certain brain states, particularly during sleep, are essential for learning and memory consolidation. Oscillatory activity plays a crucial role on several spatial scales, for example in plasticity at a synaptic level or in communication across brain areas. However, the underlying mechanisms and computational rules linking brain states and rhythms to learning, though relevant for our understanding of brain function and therapeutic approaches in brain disease, have not yet been elucidated. Here we review known mechanisms of how brain states mediate and modulate learning by their characteristic rhythmic signatures. To understand the critical interplay between brain states, brain rhythms, and learning processes, a wide range of experimental and theoretical work in animal models and human subjects from the single synapse to the large-scale cortical level needs to be integrated. By discussing results from experiments and theoretical approaches, we illuminate new avenues for utilizing neuronal learning mechanisms in developing tools and therapies, e.g. for stroke patients and to devise memory enhancement strategies for the elderly.

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Language(s): eng - English
 Dates: 2014-11-032015-01-062015-02-26
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.3389/fncom.2015.00001
PMID: 25767445
PMC: PMC4341560
Other: eCollection 2015
 Degree: -

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Title: Frontiers in Computational Neuroscience
  Abbreviation : Front Comput Neurosci
Source Genre: Journal
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Publ. Info: Lausanne : Frontiers Research Foundation
Pages: - Volume / Issue: 9 Sequence Number: 1 Start / End Page: - Identifier: Other: 1662-5188
CoNE: https://pure.mpg.de/cone/journals/resource/1662-5188