"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"

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• Neuronal responses can be described as gradient descent on variational free energy.claim0.899
  Central claim: gradient descent on free energy is a valid process-level description of neural activity.
• Can neuronal responses be described as a gradient descent on variational free energy?question0.890
  Central research question: whether process-level neural dynamics conform to free energy minimization.
• The dynamics of synaptic plasticity follow a descent on the gradient of variational free energy.claim0.838
  Learning as free energy gradient descent, Section 8.
• All neuronal processing and action selection minimize variational free energy, unifying perception, action, and learning.claim0.833
  Fundamental assertion: single imperative (free energy minimization) explains diverse cognitive and neural phenomena.
• Gradient Descent on Variational Free Energymethod0.804
  Process by which neuronal dynamics minimize free energy; produces empirically observable neural phenomena.
• The flow of internal and active states can be described as a gradient descent on variational free energy.claim0.795
  A formal result from the proof.
• Gradient Descent on Free Energymethod0.787
  Optimization procedure for simultaneously updating action selection and perception; uses step size ζ (default 4).
• 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.hypothesis0.785
  The core process theory hypothesis set up in the paper.