Neuronal dynamics for state estimation coincide with variational message passing.

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• What are the neuronal mechanisms by which prior beliefs from one agent's model are received and properly implemented by a naive agent (neuronal hermeneutics)?question0.798
  Open question about inter-agent communication beyond model-space assumption
• All neuronal processing and action selection minimize variational free energy, unifying perception, action, and learning.claim0.797
  Fundamental assertion: single imperative (free energy minimization) explains diverse cognitive and neural phenomena.
• Neuronal responses can be described as gradient descent on variational free energy.claim0.785
  Central claim: gradient descent on free energy is a valid process-level description of neural activity.
• altered dynamical regimes in neural systemsconcept0.777
  Empirical prediction of the framework regarding brain dynamics after emptiness realisation.
• "the fact that a gradient descent appears to be a valid description of neuronal activity means that variational free energy is a Lyapunov function for neuronal dynamics"concept0.777
  Key theoretical result: gradient descent formulation validates free energy as fundamental principle.
• Biological agents use a process theory of active inference where neuronal dynamics correspond to variational free energy minimisation for perception and expected free energy minimisation for action.hypothesis0.771
  The core process theory hypothesis set up in the paper.
• Can neuronal responses be described as a gradient descent on variational free energy?question0.764
  Central research question: whether process-level neural dynamics conform to free energy minimization.
• Under the Markov blanket assumption together with NESS, a generalised synchrony appears, such that the dynamics of internal states can be cast as performing inference over external states via minimisation of variational free energy.claim0.757
  Key theoretical claim linking active inference to physics in Section 2.