Neural computationJul 27, 2026

Brain Wave Interactions as a Basis for Evaluating Prediction Errors

Psychedelic Science Weekly Brief ↗PubMed ↗DOI ↗

Updated Jul 28, 2026

Abstract

Cross-frequency coupling (CFC) may be a key computational mechanism in predictive coding.

Signal-envelope coupling (SEC) generates prediction-error signals by comparing top-down predictions with bottom-up oscillatory envelopes.
Envelope-envelope coupling (EEC) operates at slower timescales to implement a gating mechanism for precision weighting.
In Alzheimer's disease, dysfunction in interneurons disrupts the comparator mechanism, leading to abnormal prediction error responses.
Serotonergic psychedelics increase excitatory gain, which diminishes the effect of predictions and leads to sensory processing failures.
These findings suggest that alterations in CFC mechanisms could play a role in both normal brain function and various diseases.

Simplified

Predictive coding frameworks suggest neural computations rely on hierarchical error minimization, yet the neural implementation of this inference remains unclear. We propose that cross-frequency coupling (CFC) furnishes fundamental mechanism for this process. Using our laminar neural mass model (LaNMM), we demonstrate that signal-envelope coupling (SEC) an envelope-envelope coupling (EEC) instantiate a hierarchical comparator mechanism. Specifically, SEC generates prediction-error signals by subtracting top-down predictions from bottom-up oscillatory envelopes, while EEC operates at slower timescales to implement gating-a critical mechanism for precision weighting. To establish the face validity and clinical implications of this proposal, we model perturbations of these CFC mechanisms to investigate their roles in pathophysiological and altered neuronal function. In Alzheimer's disease, interneuron dysfunction disrupts the comparator, causing aberrant prediction error amplification in early stages and drastic attenuation in late phases. Conversely, serotonergic psychedelics increase excitatory gain, diminishing the modulatory effect of predictions and causing a failure of sensory attenuation. Collectively, these findings implicate multiscale cross-frequency coupling as a key computational mechanism supporting predictive coding and its disruption in disease.

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Brain Wave Interactions as a Basis for Evaluating Prediction Errors

Abstract

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