Traveling waves shape neural population dynamics enabling predictions and internal model updating

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Neuroscience

Background:

• The brain continuously generates predictions about the environment based on learned statistical regularities.
• The precise mechanisms by which these predictive models are optimized through real-time environmental interactions remain incompletely understood.
• Traveling waves (TWs), propagating neural activity patterns, are hypothesized to play a role in predictive processing by modulating cortical excitability.

Approach:

• Utilized human intracranial recordings to investigate the role of neural oscillations in predictive processing.
• Analyzed anterior-to-posterior and posterior-to-anterior alpha traveling waves (TWs) in relation to prediction strength and learning.
• Examined neural state space trajectories, alpha phase resets, and phase-amplitude coupling between alpha and gamma oscillations during learning.

Key Points:

• Anterior-to-posterior alpha TWs were found to correlate with the strength of ongoing predictions.
• Learning-induced alterations in neural state space trajectories were linked to trial-by-trial prediction accuracy.
• Prediction errors, signaled by mismatches between predictions and sensory evidence, triggered alpha phase resets and enhanced alpha-gamma coupling in the lateral temporal cortex.

Conclusions:

• Alpha TWs play a dual role: conveying predictions to sensory areas and transmitting prediction error signals to frontal regions.
• These alpha TWs facilitate the fine-tuning of predictive models on a trial-by-trial basis.
• The findings elucidate a neural mechanism for iterative predictive model updating in the brain.