concept
active
concept:lyapunov-functionLyapunov Function
Variational free energy acts as Lyapunov function for neuronal dynamics, ensuring convergence.
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Frameworks (1)
framework
- Free Energy Principleassociated_withA foundational variational principle from statistical physics that formalizes how self-organizing systems maintain structural integrity and adapt to their environment by minimizing free energy—a mathematical bound on surprise or prediction error. Originally developed by Karl Friston, the framework unifies action, perception, and learning as processes of active inference, where systems both update internal models of the world and act upon it to reduce the divergence between predictions and observations.
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Metric directly localizing saddle-like boundaries between solution routes by measuring maximal local trajectory separation.
- Spectrum quantifying amplification/suppression of perturbations along independent latent directions, used to detect transient chaos onset during training.
- Links saddle-crossing intensity to the number of candidate-answer switches during reasoning.
- In machine learning, a function measuring the distance between current and desired output; analogous to stress.
- Quantitative metric of closeness of embryo to target pattern; d = (1/N²) Σ(E_ij - T_ij)² with exponential scaling d' = 9^d.
- In RL, a scalar signal from the environment that defines the agent's goal; in active inference, reward is just another observation with associated preference.
- Assignment and contents functions for state manipulation in Algol 50, from McCarthy 1963.
- The practical, working aspect of a building; reinterpreted as the dynamic harmony of moving centers.