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.
Highlights
BRIDGING BRAIN SCALES Learning refers to the ability of nervous systems to adapt to changing internal and external conditions, and includes perceptual processes of information uptake as wellAbbreviations: ACh, Acetylcholine; blood oxygen level dependent (BOLD), Blood oxygen level dependent; CA, Cornu Ammonis of hippocampus; DC, Direct current; entorhinal cortex (EC), Entorhinal cortex; EEG, Electroencephalography; fMRI, functional magnetic resonance imaging; HFO, High-frequency oscillation; LTD, Long-term depression; longterm potentiation (LTP), Long-term potentiation; MEG, Magnetencephalography; NE, Norepinephrine; NMDA, N-methyl-D-aspartate; perceptual learning (PL), Perceptual learning; REM, Rapid eye movement; resting-state networks (RSN), Resting state network; S1, Primary somatosensory cortex; slow oscillation (SO), Slow oscillations; SPW-Rs, Sharp wave ripples; STDP, Spike-timing dependent plasticity; SWS, Slow-wave sleep; transcranial direct current stimulation (tDCS), Transcranial direct current stimulation.as the storage of information
These data suggest that the cortical networks that are used during wakefulness for the up-take of hippocampus-dependent episodic information, thereby producing theta activity, are functionally linked to the networks that are used during succeeding periods of SWS to consolidate this memory and thereby produce SOs
A single dose of amphetamine (Dinse et al, 2003) results in almost a twofold increase in both the normally observed improvement of tactile acuity and the cortical reorganization. These findings indicate that the processes underlying repetitive stimulation are further controlled by neuromodulatory systems
Summary
Petra Ritter 1,2,3,4*, Jan Born 5, Michael Brecht 3, Hubert R. Oscillatory activity plays a crucial role on several spatial scales, for example in plasticity at a synaptic level or in communication across brain areas. 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. 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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