Sleep implements Bayesian model reduction: non-REM sleep performs synaptic regression (complexity reduction) and REM sleep re-evaluates posteriors under the new reduced model.

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• Sleep (Bayesian model reduction) should improve performance on rule-learning tasks, with the prevalence of insight-dependent performance changes roughly doubling after nocturnal sleep.hypothesis0.869
  Prediction consistent with Wagner et al. (2004) finding; extended to the active inference account of sleep
• Structure learning via Bayesian model reduction has a clear biological interpretation in terms of synaptic decay and switching off certain synaptic connections, reminiscent of REM sleep.claim0.850
  Biological interpretation of Bayesian model reduction.
• Bayesian model reduction after 12 trials correctly removes off-diagonal (redundant) parameters from the likelihood array, recovering the true contingency structure.finding0.776
  Validation that BMR correctly identifies and prunes wrong connections in the likelihood mapping
• Non-REM Sleepconcept0.761
  Associated with synaptic homeostasis and elimination of redundant model parameters (Bayesian model reduction)
• Bayesian model reduction formalises post-hoc hypothesis testing to simplify the generative model.claim0.754
  Definition of Bayesian model reduction, Section 9.1.
• Buddhist awakening can be formalised as Bayesian model reduction of the separation prior sigma, yielding a post-dual agent with unconstrained QRF deploymentsclaim0.746
  Central theoretical contribution of the paper unifying contemplative path with active inference framework
• Bayesian Model Reduction (BMR)framework0.741
  Formal mechanism by which the separation prior sigma is pruned from the generative model
• REM Sleepconcept0.736
  Associated with reevaluation of posteriors under new model structure after non-REM synaptic regression