claim

active

claim:all-neuronal-processing-and-action-selection-minimize-variational-free-energy-unifying-perception-action-and-learning

All neuronal processing and action selection minimize variational free energy, unifying perception, action, and learning.

Fundamental assertion: single imperative (free energy minimization) explains diverse cognitive and neural phenomena.

Source paper

extracted_from

Active Inference: A Process Theory

(2017) · Karl Friston · Thomas FitzGerald · Francesco Rigoli · Philipp Schwartenbeck +1

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Communities (3)

community

Causal emergence in biological systems
members_of
Examines how macro-scale causal power exceeds micro-scale in living and learning systems.
Active inference & free energy minimization
members_of
Friston's framework unifying perception, action, and learning under variational free energy minimization.
Bioelectric computation and morphological intelligence
members_of
Systems across kingdoms (neurons, plants, salamanders, xenobots) use bioelectric signaling to solve morphogenesis and behavior, independent of genetic specification.

Methods (1)

method

Gradient Descent on Variational Free Energy
supports
Process by which neuronal dynamics minimize free energy; produces empirically observable neural phenomena.

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

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.claim0.862
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?question0.836
Central research question: whether process-level neural dynamics conform to free energy minimization.
"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"concept0.833
Key theoretical result: gradient descent formulation validates free energy as fundamental principle.
Agents perceive by minimizing variational free energy to ensure model consistency with past observations and act by minimizing expected free energy to make future sensations consistent with preferences.claim0.830
Formalization of perception-action cycle integrating inference and decision-making.
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.hypothesis0.825
The core process theory hypothesis set up in the paper.
Acting to optimize value and perception are two aspects of exactly the same principle: minimization of free energy.claim0.819
Foundational claim unifying action and perception within single optimization framework.
The dynamics of synaptic plasticity follow a descent on the gradient of variational free energy.claim0.815
Learning as free energy gradient descent, Section 8.
Variational free energy measures the fit between an internal (generative) model of its sensations and sensory observations.claim0.802
Definitional claim from Section 2.

Cross-corpus bridges (2)

same_concept_as · Nomic cosine

External markdown files that talk about the same concept as this entity.

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Free Energy Principle And Active Inferenceframeworks/free-energy-principle-and-active-inference.md0.839
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How frozen is the behavior of cortical regions, and can neurons from one modality be trained to control novel functions outside their evolved purpose?questions/how-frozen-is-the-behavior-of-cortical-regions.md0.808