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Cross-Frequency Coupling as a Neural Substrate for Prediction Error Evaluation: A Laminar Neural Mass Modeling
Giulio Ruffini1,2, Edmundo Lopez-Sola1,3, Raul Palma4,5
1Neuroelectrics, 08035, Barcelona, Spain.
Neural Computation
|July 27, 2026
Summary
Cross-frequency coupling (CFC) may implement predictive coding through hierarchical error minimization. This mechanism, modeled using signal-envelope coupling (SEC) and envelope-envelope coupling (EEC), is disrupted in Alzheimer's disease and by psychedelics.
Area of Science:
- Computational neuroscience
- Systems neuroscience
Background:
- Predictive coding theories propose hierarchical error minimization in neural computation.
- The precise neural mechanisms underlying this inference remain largely unknown.
Purpose of the Study:
- To propose cross-frequency coupling (CFC) as a fundamental mechanism for predictive coding.
- To model the neural implementation of hierarchical error minimization using CFC.
Main Methods:
- Development of a laminar neural mass model (LaNMM).
- Modeling signal-envelope coupling (SEC) and envelope-envelope coupling (EEC) as comparator mechanisms.
- Simulating perturbations of CFC in models of Alzheimer's disease and psychedelic effects.
Main Results:
- SEC and EEC instantiate a hierarchical comparator, with SEC generating prediction errors and EEC enabling precision weighting.
- Interneuron dysfunction in Alzheimer's disease leads to aberrant prediction error signaling.
- Psychedelics disrupt sensory attenuation by reducing the influence of predictions.
Conclusions:
- Multiscale cross-frequency coupling is implicated as a key computational mechanism for predictive coding.
- Disruptions in CFC contribute to the pathophysiology of Alzheimer's disease and altered states induced by psychedelics.
