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Front Comput Neurosci. 2015 Feb 26;9:1. doi: 10.3389/fncom.2015.00001. eCollection 2015.

State-dependencies of learning across brain scales.

Author information

  • 1Minerva Research Group BrainModes, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany ; Department of Neurology, Charité University Medicine Berlin Berlin, Germany ; Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin Berlin, Germany ; Berlin School of Mind and Brain & Mind and Brain Institute, Humboldt-Universität zu Berlin Berlin, Germany.
  • 2Department of Medical Psychology and Behavioral Neurobiology & Center for Integrative Neuroscience (CIN), University of Tübingen Tübingen, Germany.
  • 3Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin Berlin, Germany.
  • 4Neural Plasticity Lab, Institute for Neuroinformatics, Ruhr-University Bochum Bochum, Germany ; Department of Neurology, BG University Hospital Bergmannsheil, Ruhr-University Bochum Bochum, Germany.
  • 5Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin Berlin, Germany ; NeuroCure Cluster of Excellence Berlin, Germany.
  • 6Clinic for Cognitive Neurology, University Hospital Leipzig Leipzig, Germany ; Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany.
  • 7Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin Berlin, Germany ; NeuroCure Cluster of Excellence Berlin, Germany ; Neuroscience Research Center NWFZ, Charité University Medicine Berlin Berlin, Germany ; Max-Delbrück Center for Molecular Medicine, MDC Berlin, Germany ; Center for Neurodegenerative Diseases (DZNE) Berlin, Germany.
  • 8Bernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin Berlin, Germany ; Department of Biology, Institute for Theoretical Biology (ITB), Humboldt-Universität zu Berlin Berlin, Germany.
  • 9Berlin School of Mind and Brain & Mind and Brain Institute, Humboldt-Universität zu Berlin Berlin, Germany ; Clinic for Cognitive Neurology, University Hospital Leipzig Leipzig, Germany ; Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany.

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.

KEYWORDS:

brain scales; computational modeling; learning; plasticity; state-dependencies

PMID:
25767445
PMCID:
PMC4341560
DOI:
10.3389/fncom.2015.00001

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