Karl John Friston

The paper investigates how the hippocampal-entorhinal system transforms abstract decision spaces by analyzing fMRI data during preference comparison tasks.

This study investigates the neural mechanisms underlying transitions in cortical beta power in Parkinsonian rats using dynamic causal modeling and identifies key thalamocortical coupling patterns.

This study uses dynamic causal modelling to analyze fluctuations in resting-state EEG connectivity, finding both subject-specific and common components in network connectivity patterns.

This paper models the locus coeruleus (LC) and noradrenaline (NA) system using Active Inference to show that LC activity reflects prediction errors, enabling adaptive learning and cognitive flexibility.

The paper challenges the proportional recovery rule by showing that baseline score correlations can mislead about outcome predictability in stroke recovery.

The paper tests if in vitro neuronal responses align with Bayesian ideal observer models based on free energy minimization.

This study shows NMDA-receptor antibodies alter cortical microcircuit dynamics and predispose to paroxysmal EEG abnormalities in a mouse model.

This study identifies a distinct functional connectivity mode during movie-watching that reflects individual differences and is stable over time.

This study evaluates the reliability of effective connectivity in the default mode network using longitudinal fMRI data and finds that hemispheric asymmetry and Bayesian model reduction improve consistency.

The paper uses imaging and dynamic causal modelling to show concurrent changes in synaptic coupling at macro- and mesoscale during epileptic seizures in zebrafish.

This study investigates the effects of NMDAR blockade with ketamine on auditory processing using a roving oddball paradigm and dynamic causal modelling, finding selective prefrontal disinhibition.

This paper formulates planning and navigation as active inference, showing agents solve maze problems by minimizing uncertainty through context-sensitive prior preferences and subgoal formation.

The paper proposes a model for social intelligence that uses multiple internal models to infer and learn from conspecific interactions, demonstrating success in a synthetic bird learning zebra finch songs.

The paper reviews theories of homeostatic control and evaluates their application to energy homeostasis, proposing a circuit that integrates physiological and behavioral regulation.

The paper proposes and validates an alternative hypothesis for anticipatory control through sensory predictions rather than motor error-based inverse models in a simulated task.

This paper demonstrates that Markov blankets emerge at all levels of biological systems through simulations based on variational free energy minimisation, supporting self-organisation and autopoietic behaviour.

The paper uses fMRI data from movie watching to infer effective connectivity, finding that task-related changes affect local activity more than connectivity.

This study uses whole-brain computational modeling to analyze how deep brain stimulation affects brain dynamics in Parkinson’s disease patients, finding it shifts brain states closer to healthy dynamics.

This study investigates genetic and environmental contributions to the association between in-scanner head motion and default network intrinsic functional connectivity using a twin dataset.