The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models

• Published in: Frontiers in computational neuroscience Vol. 8; p. 36
• Main Authors: Sigala, Rodrigo, Haufe, Sebastian, Roy, Dipanjan, Dinse, Hubert R., Ritter, Petra
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 04.04.2014; Frontiers Media S.A
• Subjects: 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; Germany; memory; cognition; alpha rhythm; attention; learning; large-scale modeling; oscillations
• ISSN: 1662-5188; 1662-5188
• DOI: 10.3389/fncom.2014.00036

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.