ReviewEntropy (Basel, Switzerland)2022
The Free Energy Principle for Perception and Action: A Deep Learning Perspective.
Review in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 39 citations in OpenAlex.
- Active inference and artificial reasoning.Nature communications · 2026Article
- Prediction error correlates in the striosome-dopamine circuit emerge from information gain.Nature communications · 2026Article
- Active inference as a model of collision avoidance behavior in human drivers.Nature communications · 2026Article
- The phantastic organ: predictive processing in the brain.Frontiers in psychology · 2026Review
- Sleep bruxism: a novel theoretical perspective through the lens of the free energy principle (FEP).Frontiers in behavioral neuroscience · 2026Article
- Mark the Unexpected! Animacy Preference and Directed Movement in Visual Language.Cognitive science · 2025Article
- Article
- Oscillating latent dynamics in robot systems during walking and reaching.Scientific reports · 2024Article
- Resolving uncertainty on the fly: modeling adaptive driving behavior as active inference.Frontiers in neurorobotics · 2024Article
- Deep kinematic inference affords efficient and scalable control of bodily movements.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Bio-Inspired Intelligent Systems: Negotiations between Minimum Manifest Task Entropy and Maximum Latent System Entropy in Changing Environments.Entropy (Basel, Switzerland) · 2023Article
- Recursive neural programs: A differentiable framework for learning compositional part-whole hierarchies and image grammars.PNAS nexus · 2023Article
- Incremental Learning of Goal-Directed Actions in a Dynamic Environment by a Robot Using Active Inference.Entropy (Basel, Switzerland) · 2023Article
- Free energy and inference in living systems.Interface focus · 2023Article
- Symmetry and complexity in object-centric deep active inference models.Interface focus · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The free energy principle, and its corollary active inference, constitute a bio-inspired theory that assumes biological agents act to remain in a restricted set of preferred states of the world, i.e., they minimize their free energy. Under this principle, biological agents learn a generative model of the world and plan actions in the future that will maintain the agent in an homeostatic state that satisfies its preferences. This framework lends itself to being realized in silico, as it comprehends important aspects that make it computationally affordable, such as variational inference and amortized planning. In this work, we investigate the tool of deep learning to design and realize artificial agents based on active inference, presenting a deep-learning oriented presentation of the free energy principle, surveying works that are relevant in both machine learning and active inference areas, and discussing the design choices that are involved in the implementation process. This manuscript probes newer perspectives for the active inference framework, grounding its theoretical aspects into more pragmatic affairs, offering a practical guide to active inference newcomers and a starting point for deep learning practitioners that would like to investigate implementations of the free energy principle.
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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.