Rosalyn Moran,
Department of Neuroimaging,
King’s College, London, UK.

Supporting documents

The videos are here:

  • First part (0h54:53):
  • Second part (0h48:37):
  • Third part (Q&A session; 0h31:03):


The theory of Active Inference proposes that all biological agents retain self-ness by minimizing their long-term average surprisal. In information theoretic terms, Free Energy provides a soluble approximation to this long-term surprise ‘now’ and necessitates the development of a generative model of the environment within the agent itself. The minimization of this quantity via a gradient flow is purported to be the purpose of neuronal activity in the brain and thus provides a mapping from brain activity to their first-principle computations. 

In this talk I will outline the theory of Active Inference and describe how discrete and continuous-time systems that perceive and act can be built in silico, while providing evidence for these implementations in neurobiological and behavioral recordings. 

Using two experiments in human participants, I aim to demonstrate that human visual search and classification of the MNIST dataset (experiment 1) and world model building and adjustment in a maze task (experiment 2) can be cast as Active Inference processes that utilize neurobiologically plausible architectures comprising prediction in visual hierarchies and alterations in precision via neuromodulation. 

From experiment 1, I will show how the expected variational free energy over future states can be used by computer vision systems to decide when and where to look and that these in silico properties are observed in human visual search. By measuring eye-movement responses to the classic Machine Vision dataset – MNIST we will show that humans decide when and where to look, sample data and draw conclusions in a way commensurate with this theory. I will contrast this scheme to popular machine vision systems that use solely feedforward networks and discuss the implications for human-like computing.

From experiment 2, we will provide a flexible model updating regime that carries the hallmarks of the noradrenergic system. By performing tasks including maze search and go/no-go we can demonstrate a specific role for noradrenaline that enables an Active Inference agent to flexibly update its model of current environmental contingencies. We will show emergent features of the in silico system that mimic neuronal firing patterns of noradrenergic cells in the locus coeruleus and behaviour that mimics inter-individual search phenotypes.