Neuronal responses can be described as gradient descent on variational free energy.

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• "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"concept0.899
  Key theoretical result: gradient descent formulation validates free energy as fundamental principle.
• All neuronal processing and action selection minimize variational free energy, unifying perception, action, and learning.claim0.862
  Fundamental assertion: single imperative (free energy minimization) explains diverse cognitive and neural phenomena.
• The dynamics of synaptic plasticity follow a descent on the gradient of variational free energy.claim0.858
  Learning as free energy gradient descent, Section 8.
• Gradient Descent on Free Energymethod0.833
  Optimization procedure for simultaneously updating action selection and perception; uses step size ζ (default 4).
• Gradient Descent on Variational Free Energymethod0.832
  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.claim0.817
  A formal result from the proof.
• Variational free energy measures the fit between an internal (generative) model of its sensations and sensory observations.claim0.798
  Definitional claim from Section 2.
• 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.hypothesis0.797
  The core process theory hypothesis set up in the paper.