119 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University College LondonGB10 papers
- Wellcome Centre for Human NeuroimagingGB10 papers
- Stony Brook UniversityUS7 papers
- Imperial College LondonGB6 papers
- Max Planck Institute of Animal BehaviorDE6 papers
- University of KonstanzDE6 papers
- MRC Brain Network Dynamics UnitGB4 papers
- University of OxfordGB4 papers
- Eindhoven University of TechnologyNL3 papers
- Advanced Projects Research Incorporated (United States)US2 papers
- Monash UniversityAU2 papers
- National Hospital for Neurology and NeurosurgeryGB2 papers
19 papers
Geometric Hyperscanning of Affect under Active Inference
Nicolas Hinrichs, Mahault Albarracin, Dimitris Bolis +3
Second-person neuroscience holds social cognition as embodied meaning co-regulation through reciprocal interaction, modeled here as coupled active inference with affect emerging as…
Development of Digital Twin Environment through Integration of Commercial Metaverse Platform and IoT Sensors of Smart Building
Yusuke Masubuchi, Takefumi Hiraki, Yuichi Hiroi +4
The digital transformation of smart cities and workplaces requires effective integration of physical and cyber spaces, yet existing digital twin solutions remain limited in support…
Exploring and Learning Structure: Active Inference Approach in Navigational Agents
Daria de Tinguy, Tim Verbelen, Bart Dhoedt
Drawing inspiration from animal navigation strategies, we introduce a novel computational model for navigation and mapping, rooted in biologically inspired principles. Animals exhi…
Spatial and Temporal Hierarchy for Autonomous Navigation using Active Inference in Minigrid Environment
Daria de Tinguy, Toon van de Maele, Tim Verbelen +1
Robust evidence suggests that humans explore their environment using a combination of topological landmarks and coarse-grained path integration. This approach relies on identifiabl…
Bayesian sparsification for deep neural networks with Bayesian model reduction
Dimitrije Marković, Karl J. Friston, Stefan J. Kiebel
Deep learning's immense capabilities are often constrained by the complexity of its models, leading to an increasing demand for effective sparsification techniques. Bayesian sparsi…
On efficient computation in active inference
Aswin Paul, Noor Sajid, Lancelot Da Costa +1
Despite being recognized as neurobiologically plausible, active inference faces difficulties when employed to simulate intelligent behaviour in complex environments due to its comp…
Collective behavior from surprise minimization
Conor Heins, Beren Millidge, Lancelot da Costa +3
Collective motion is ubiquitous in nature; groups of animals, such as fish, birds, and ungulates appear to move as a whole, exhibiting a rich behavioral repertoire that ranges from…
Realising Synthetic Active Inference Agents, Part II: Variational Message Updates
Thijs van de Laar, Magnus Koudahl, Bert de Vries
The Free Energy Principle (FEP) describes (biological) agents as minimising a variational Free Energy (FE) with respect to a generative model of their environment. Active Inference…
Designing Ecosystems of Intelligence from First Principles
Karl J Friston, Maxwell J D Ramstead, Alex B Kiefer +17
This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosyste…
Path integrals, particular kinds, and strange things
Karl Friston, Lancelot Da Costa, Dalton A. R. Sakthivadivel +4
This paper describes a path integral formulation of the free energy principle. The ensuing account expresses the paths or trajectories that a particle takes as it evolves over time…
One person's modus ponens...: Comment on "The Markov blanket trick: On the scope of the free energy principle and active inference" by Raja and colleagues (2021)
Maxwell J. D Ramstead
In this comment on "The Markov blanket trick: On the scope of the free energy principle and active inference" by Raja and colleagues (2021) in Physics of Life Reviews, I argue that…
A Worked Example of the Bayesian Mechanics of Classical Objects
Dalton A R Sakthivadivel
Bayesian mechanics is a new approach to studying the mathematics and physics of interacting stochastic processes. Here, we provide a worked example of a physical mechanics for clas…
Regarding Flows Under the Free Energy Principle: A Comment on "How Particular is the Physics of the Free Energy Principle?" by Aguilera, Millidge, Tschantz, and Buckley
Dalton A R Sakthivadivel
In a recent technical critique of the free energy principle (FEP) due to Aguilera-Millidge-Tschantz-Buckley, it is argued that there are a number of instances where the FEP$\unicod…
Sparse coupling and Markov blankets: A comment on "How particular is the physics of the Free Energy Principle?" by Aguilera, Millidge, Tschantz and Buckley
Conor Heins, Lancelot Da Costa
In this commentary, we respond to a technical analysis of the Free Energy Principle (hereafter: FEP) presented in "How particular is the physics of the Free Energy Principle" by Ag…
Particular flows and attracting sets: A comment on "How particular is the physics of the Free Energy Principle?" by Aguilera, Millidge, Tschantz and Buckley
Conor Heins
In this commentary, I expand on the analysis of the recent article "How particular is the physics of the Free Energy Principle?" by Aguilera et al. by studying the flow fields of l…
On Bayesian Mechanics: A Physics of and by Beliefs
Maxwell J. D. Ramstead, Dalton A. R. Sakthivadivel, Conor Heins +5
The aim of this paper is to introduce a field of study that has emerged over the last decade called Bayesian mechanics. Bayesian mechanics is a probabilistic mechanics, comprising…
Some Minimal Notes on Notation and Minima: A Comment on "How Particular is the Physics of the Free Energy Principle?" by Aguilera, Millidge, Tschantz, and Buckley
Maxwell J D Ramstead, Dalton A R Sakthivadivel
We comment on a technical critique of the free energy principle in linear systems by Aguilera, Millidge, Tschantz, and Buckley, entitled "How Particular is the Physics of the Free…
pymdp: A Python library for active inference in discrete state spaces
Conor Heins, Beren Millidge, Daphne Demekas +4
Active inference is an account of cognition and behavior in complex systems which brings together action, perception, and learning under the theoretical mantle of Bayesian inferenc…
Formalising the Use of the Activation Function in Neural Inference
Dalton A R Sakthivadivel
We investigate how the activation function can be used to describe neural firing in an abstract way, and in turn, why it works well in artificial neural networks. We discuss how a…