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
5 papers · 1 filter
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…