ArticleEntropy (Basel, Switzerland)2021
Neural Dynamics under Active Inference: Plausibility and Efficiency of Information Processing.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Limb apraxia and active inference in the visuomotor pathways.Psychonomic bulletin & review · 2026Review
- A Rosetta Stone hypothesis for neurophenomenology: mathematical predictions from predictive processing.Neuroscience of consciousness · 2026Article
- Energy optimization induces predictive-coding properties in a multi-compartment spiking neural network model.PLoS computational biology · 2025Article
- Eigenmodes of the deep unconscious: the neuropsychology of Jungian archetypes and psychedelic experience.Neuroscience of consciousness · 2025Review
- Article
- Predictive coding and stochastic resonance as fundamental principles of auditory phantom perception.Brain : a journal of neurology · 2023Review
- A deep hierarchy of predictions enables online meaning extraction in a computational model of human speech comprehension.PLoS biology · 2023Article
- Predictive coding is a consequence of energy efficiency in recurrent neural networks.Patterns (New York, N.Y.) · 2022Article
- A step-by-step tutorial on active inference and its application to empirical data.Journal of mathematical psychology · 2022Article
- How particular is the physics of the free energy principle?Physics of life reviews · 2022Review
- An active inference model of conscious access: How cognitive action selection reconciles the results of report and no-report paradigms.Current research in neurobiology · 2022Article
- Information Geometry, Fluctuations, Non-Equilibrium Thermodynamics, and Geodesics in Complex Systems.Entropy (Basel, Switzerland) · 2021Review
- Memory and Markov Blankets.Entropy (Basel, Switzerland) · 2021Article
- Information and Self-Organization II: Steady State and Phase Transition.Entropy (Basel, Switzerland) · 2021Article
- Understanding, Explanation, and Active Inference.Frontiers in systems neuroscience · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Active inference is a normative framework for explaining behaviour under the free energy principle-a theory of self-organisation originating in neuroscience. It specifies neuronal dynamics for state-estimation in terms of a descent on (variational) free energy-a measure of the fit between an internal (generative) model and sensory observations. The free energy gradient is a prediction error-plausibly encoded in the average membrane potentials of neuronal populations. Conversely, the expected probability of a state can be expressed in terms of neuronal firing rates. We show that this is consistent with current models of neuronal dynamics and establish face validity by synthesising plausible electrophysiological responses. We then show that these neuronal dynamics approximate natural gradient descent, a well-known optimisation algorithm from information geometry that follows the steepest descent of the objective in information space. We compare the information length of belief updating in both schemes, a measure of the distance travelled in information space that has a direct interpretation in terms of metabolic cost. We show that neural dynamics under active inference are metabolically efficient and suggest that neural representations in biological agents may evolve by approximating steepest descent in information space towards the point of optimal inference.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.