161 citations · 673 across the 32 of their papers we have counts for
33 papers
The Complex Brain Hypothesis: Resolving the Entropy-Content Conundrum in Minimal Phenomenal Experience
Jonas Mago, Edmundo Lopez-Sola, Jakub Vohryzek +4
Minimal Phenomenal Experiences (MPEs) are states of consciousness in which wakefulness is preserved but phenomenal content is low or absent. The Entropic Brain Hypothesis (EBH) is…
EcoNet: Multiagent Planning and Control Of Household Energy Resources Using Active Inference
John C. Boik, Kobus Esterhuysen, Jacqueline B. Hynes +5
Advances in automated systems afford new opportunities for intelligent management of energy at household, local area, and utility scales. Home Energy Management Systems (HEMS) can…
Hierarchical generative modelling for autonomous robots
Kai Yuan, Noor Sajid, Karl Friston +1
Humans can produce complex whole-body motions when interacting with their surroundings, by planning, executing and combining individual limb movements. We investigated this fundame…
Brain in the Dark: Design Principles for Neuro-mimetic Learning and Inference
Mehran H. Bazargani, Szymon Urbas, Karl Friston
Even though the brain operates in pure darkness, within the skull, it can infer the most likely causes of its sensory input. An approach to modelling this inference is to assume th…
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…
Designing explainable artificial intelligence with active inference: A framework for transparent introspection and decision-making
Mahault Albarracin, Inês Hipólito, Safae Essafi Tremblay +4
This paper investigates the prospect of developing human-interpretable, explainable artificial intelligence (AI) systems based on active inference and the free energy principle. We…
Spectral Dynamic Causal Modelling: A Didactic Introduction and its Relationship with Functional Connectivity
Leonardo Novelli, Karl Friston, Adeel Razi
We present a didactic introduction to spectral Dynamic Causal Modelling (DCM), a Bayesian state-space modelling approach used to infer effective connectivity from non-invasive neur…
The inner screen model of consciousness: applying the free energy principle directly to the study of conscious experience
Maxwell J. D. Ramstead, Mahault Albarracin, Alex Kiefer +4
This paper presents a model of consciousness that follows directly from the free-energy principle (FEP). We first rehearse the classical and quantum formulations of the FEP. In par…
Curvature-Sensitive Predictive Coding with Approximate Laplace Monte Carlo
Umais Zahid, Qinghai Guo, Karl Friston +1
Predictive coding (PC) accounts of perception now form one of the dominant computational theories of the brain, where they prescribe a general algorithm for inference and learning…
A variational synthesis of evolutionary and developmental dynamics
Karl Friston, Daniel Ari Friedman, Axel Constant +3
This paper introduces a variational formulation of natural selection, paying special attention to the nature of "things" and the way that different "kinds" of "things" are individu…
Control flow in active inference systems
Chris Fields, Filippo Fabrocini, Karl Friston +4
Living systems face both environmental complexity and limited access to free-energy resources. Survival under these conditions requires a control system that can activate, or deplo…
Modelling cortical network dynamics
Gerald K. Cooray, Richard E. Rosch, Karl J. Friston
We consider the theoretical constraints on interactions between coupled cortical columns. Each column comprises a set of neural populations, where each population is modelled as a…
Bistable perception, precision and neuromodulation
Filip Novicky, Thomas Parr, Karl Friston +2
Bistable perception follows from observing a static, ambiguous, (visual) stimulus with two possible interpretations. Here, we present an active (Bayesian) inference account of bist…
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…
On the map-territory fallacy fallacy
Maxwell J D Ramstead, Dalton A R Sakthivadivel, Karl J Friston
This paper presents a meta-theory of the usage of the free energy principle (FEP) and examines its scope in the modelling of physical systems. We consider the so-called `map-territ…
The Free Energy Principle drives neuromorphic development
Chris Fields, Karl Friston, James F. Glazebrook +2
We show how any system with morphological degrees of freedom and locally limited free energy will, under the constraints of the free energy principle, evolve toward a neuromorphic…
Predictive coding and stochastic resonance as fundamental principles of auditory perception
Achim Schilling, William Sedley, Richard Gerum +7
How is information processed in the brain during perception? Mechanistic insight is achieved only when experiments are employed to test formal or computational models. In analogy t…
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…
Reclaiming saliency: rhythmic precision-modulated action and perception
Ajith Anil Meera, Filip Novicky, Thomas Parr +3
Computational models of visual attention in artificial intelligence and robotics have been inspired by the concept of a saliency map. These models account for the mutual informatio…
Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents
Alessandro Barp, Lancelot Da Costa, Guilherme França +4
In this chapter, we identify fundamental geometric structures that underlie the problems of sampling, optimisation, inference and adaptive decision-making. Based on this identifica…
Frontal effective connectivity increases with task demands and time on task: a Dynamic Causal Model of electrocorticogram in macaque monkeys
Katharina Wegner, Charles R. E. Wilson, Emmanuel Procyk +4
We apply Dynamic Causal Models to electrocorticogram recordings from two macaque monkeys performing a problem-solving task that engages working memory, and induces time-on-task eff…
The free energy principle made simpler but not too simple
Karl Friston, Lancelot Da Costa, Noor Sajid +4
This paper provides a concise description of the free energy principle, starting from a formulation of random dynamical systems in terms of a Langevin equation and ending with a Ba…
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…
A free energy principle for generic quantum systems
Chris Fields, Karl Friston, James F. Glazebrook +1
The Free Energy Principle (FEP) states that under suitable conditions of weak coupling, random dynamical systems with sufficient degrees of freedom will behave so as to minimize an…