The flow of internal and active states can be described as a gradient descent on variational free energy.

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Neuronal responses can be described as gradient descent on variational free energy.claim0.817
  Central claim: gradient descent on free energy is a valid process-level description of neural activity.
• The dynamics of synaptic plasticity follow a descent on the gradient of variational free energy.claim0.805
  Learning as free energy gradient descent, Section 8.
• "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"concept0.795
  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?question0.792
  Central research question: whether process-level neural dynamics conform to free energy minimization.
• Variational free energy measures the fit between an internal (generative) model of its sensations and sensory observations.claim0.780
  Definitional claim from Section 2.
• All neuronal processing and action selection minimize variational free energy, unifying perception, action, and learning.claim0.778
  Fundamental assertion: single imperative (free energy minimization) explains diverse cognitive and neural phenomena.
• Gradient Descent on Free Energymethod0.777
  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.hypothesis0.773
  The core process theory hypothesis set up in the paper.