Honorary Keynotes
Prof. Karl Friston
Queen Square Institute of Neurology, University College London
Karl Friston is a theoretical neuroscientist and authority on brain imaging. He invented statistical parametric mapping (SPM), voxel-based morphometry (VBM) and dynamic causal modelling (DCM). These contributions were motivated by schizophrenia research and theoretical studies of value-learning, formulated as the dysconnection hypothesis of schizophrenia. Mathematical contributions include Variational Laplace, Bayesian model reduction and variational filtering in generalised coordinates of motion. Friston currently works on functional brain architectures and the principles that underlie self organisation in open systems like the brain. His main contribution to theoretical biology is a free-energy principle for open systems—and its application to action and perception, i.e., active inference. Friston received the first Young Investigators Award in Human Brain Mapping (1996) and was elected a Fellow of the Academy of Medical Sciences (1999). In 2000 he was President of the international Organization of Human Brain Mapping. In 2003 he was awarded the Minerva Golden Brain Award and was elected a Fellow of the Royal Society in 2006. In 2008 he received a Medal, Collège de France. He became of Fellow of the Royal Society of Biology in 2012 and received the Weldon Memorial Prize and Medal in 2013 for contributions to mathematical biology. He was elected as a member of EMBO (excellence in the life sciences) in 2014 and the Academia Europaea in (2015). He was the 2016 recipient of the Charles Branch Award for unparalleled breakthroughs in Brain Research and the Glass Brain Award from the Organisation of Human Brain Mapping. He received the Donald O Hebb Award (International. Neural Network Society) in 2022 the International Prize for Translational Neuroscience (Gertrud Reemtsma Foundation) in 2024. He holds Honorary Doctorates from the universities of York, Zurich, Liège and Radboud University.
Prof. Karl Friston
Deep Inference
This presentation considers deep models in the brain. It builds on previous formulations of active inference to simulate behaviour and electrophysiological responses under deep (hierarchical) generative models of discrete state transitions. The structured temporal aspect of these models means that evidence is accumulated over time, enabling inferences about narratives and policies. We illustrate this behaviour in terms of Bayesian belief updating – and associated neuronal processes – to reproduce epistemic foraging for reward. These simulations reproduce these sort of delay period activity and local field potentials seen empirically; including evidence accumulation, place cell activity and transfer of dopamine responses. These simulations are presented as an example of how to use basic (first) principles to constrain our understanding of functional architectures that underwrite natural and artificial intelligence.