The dynamics of synaptic plasticity follow a descent on the gradient of variational free energy.

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• Neuronal responses can be described as gradient descent on variational free energy.claim0.858
  Central claim: gradient descent on free energy is a valid process-level description of neural activity.
• "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"concept0.838
  Key theoretical result: gradient descent formulation validates free energy as fundamental principle.
• Can neuronal responses be described as a gradient descent on variational free energy?question0.833
  Central research question: whether process-level neural dynamics conform to free energy minimization.
• All neuronal processing and action selection minimize variational free energy, unifying perception, action, and learning.claim0.815
  Fundamental assertion: single imperative (free energy minimization) explains diverse cognitive and neural phenomena.
• The flow of internal and active states can be described as a gradient descent on variational free energy.claim0.805
  A formal result from the proof.
• Gradient Descent on Variational Free Energymethod0.783
  Process by which neuronal dynamics minimize free energy; produces empirically observable neural phenomena.
• Gradient Descent on Free Energymethod0.777
  Optimization procedure for simultaneously updating action selection and perception; uses step size ζ (default 4).
• Variational free energy measures the fit between an internal (generative) model of its sensations and sensory observations.claim0.771
  Definitional claim from Section 2.