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Learning and Research

Textbook groups, videos, podcasts, courses, research resources, implementations, and technical foundations.

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Learning and Research pathway

Start with the highest-signal public links for this page, then continue through the related resource and directory views.

Start learningLearningOpen knowledge baseLearningFiltered resourcesGlobal directoryActive InferenceActivities

Learning resources point newcomers and experienced participants toward living references, livestreams, Active Inference Insights, textbook groups, readings, code implementations, and domain-specific courses.

Core Readings

Learning pathways include the 2022 textbook by Thomas Parr, Giovanni Pezzulo, and Karl J. Friston; the 2026 textbook Fundamentals of Active Inference: Principles, Algorithms, and Applications of the Free Energy Principle for Engineers by Sanjeev V. Namjoshi, now the focus of the Textbook Group's first 2026 cohort; Maxwell Ramstead's 2023 precis; Jared Tumiel's Spinning Up in Active Inference; Beren Millidge's paper repository; and work on Active Inference ontology and teams.

Start repositoryLearningFundamentals of Active Inference (MIT Press, 2026)Learningfundamentals repositoryRepositoryRamstead et al. Bayesian Mechanics precis (arXiv)ResearchSpinning Up in Active Inference and the Free Energy PrincipleResearchFEP and Active Inference papers repositoryResearch

Implementations

Implementation pathways include RxInfer.jl, PyMDP, SPM, symbolic cognitive robotics, and open repositories. These support practical modeling and experimentation.

GitHub organizationProjects

Tech-tree explorer

Explore the Active Inference learning graph

An interactive map of concepts, methods, and tools across the Active Inference and Free Energy Principle tech trees. Select a node to highlight its neighbors; filter by relationship type.

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Key surfaces

Learning and Research at a glance

Ideas and methods

Concepts and methods from the learning graph

A compact selection from the Active Inference and Free Energy Principle tech-tree nodes.

Public ideas and methods from the Active Inference concept graph.
IdeaTypeMaturitySummaryTagsTree
Action as Active InferenceApplicationEstablishedAction as the process of changing the world to fulfill predictions, minimizing free energy by acting on the environment rather than updating beliefs.action, embodied, motorActive Inference
Learning as Model UpdateApplicationEstablishedLearning as the slow update of generative model parameters to minimize long-term free energy, encompassing synaptic plasticity and structure learning.learning, model-update, plasticityActive Inference
Perception as InferenceApplicationEstablishedPerception understood as Bayesian inference — updating internal generative models to explain away prediction errors from sensory input.inference, perception, sensoryActive Inference
Active InferenceConceptEstablishedThe process theory derived from the Free Energy Principle: agents act to fulfill predictions and minimize expected free energy through perception and action.active-inference, process-theoryActive Inference
AllostasisConceptAdvancedPredictive homeostasis — anticipatory regulation that adjusts setpoints based on expected future demands rather than reacting to deviations.anticipatory, predictive, regulationFree Energy Principle
AutopoiesisConceptEstablishedSelf-producing systems as defined by Maturana and Varela — living systems that continuously produce and maintain themselves.maturana, self-production, varelaFree Energy Principle
EntropyConceptEstablishedA measure of uncertainty or disorder in a probability distribution, connecting information theory to thermodynamics.disorder, entropy, uncertaintyFree Energy Principle
Expected Free EnergyConceptEstablishedThe objective function for policy selection in active inference, combining epistemic (information-seeking) and pragmatic (goal-seeking) drives.efe, epistemic, policy-selection, pragmaticActive Inference

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Repository / Research

act_inf_metaanalysis

Audience: Researcher

Computational meta-analysis of Active Inference literature with nanopublication and knowledge-graph outputs.

TeX / 4 stars / updated 2026-05-04

researchknowledgetex
Learning and ResearchEcosystem
Repository / Research

Active_Inference_Ontology

Audience: Researcher

Ontology-oriented repository for shared Active Inference concepts and decentralized science knowledge infrastructure.

Unspecified / 14 stars / updated 2026-05-18

researchknowledgeunclassified
Learning and ResearchEcosystem
Repository / Research

AEOS

Audience: Researcher

Active Entity Ontology for Science

Unspecified / 8 stars / updated 2025-05-27

researchknowledgeunclassified
Learning and ResearchEcosystem
Repository / Research

CEREBRUM

Audience: Researcher

Case-enabled Bayesian reasoning engine for unified modeling and cognitive representations.

Python / 13 stars / updated 2026-06-09

researchknowledgepython
Learning and ResearchEcosystem

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Related pages

Continue through the site

Newcomers, learners, researchers, builders, and collaborators. Active Inference

Active Inference is the central scientific and practical framework around which the Institute organizes education, research, applications, and ecosystem support.

 Participants looking for synchronous ways to learn and contribute. Activities

Activities are the Institute's live participation surface: learning groups, model streams, project meetings, public media, and recurring updates.

 Builders, researchers, maintainers, partners, and contributors looking for concrete work. Projects

Projects convert shared ideas into repositories, research outputs, learning infrastructure, media, events, and applied work.

 Learners, students, and researchers working through Active Inference textbooks in a structured group setting. Textbook Group

The Textbook Group is a sustained Institute educational program running structured cohort-based learning through Active Inference textbooks. Since 2022 it has run 9 cohorts on the 2022 textbook 'Active Inference: The Free Energy Principle in Mind, Brain, and Behavior' by Thomas Parr, Giovanni Pezzulo, and Karl J. Friston. As of mid-2026 the group is live in its first cohort on the 2026 textbook 'Fundamentals of Active Inference: Principles, Algorithms, and Applications of the Free Energy Principle for Engineers' by Sanjeev V. Namjoshi.

 Learners, educators, and engineers working through the Fundamentals of Active Inference textbook. Fundamentals of Active Inference

Fundamentals of Active Inference is an Ecosystem project supporting the development and dissemination of the Fundamentals of Active Inference textbook — a comprehensive introduction to Active Inference principles, algorithms, and applications for engineers. The Institute hosts a Textbook Group cohort working through this book.