prof. Karl Friston.
Unfortunately, prof. Friston won't attend the conference in person and he'll give his lecture remotely.
Karl Friston is a theoretical neuroscientist and authority on brain imaging. He is a multiple award winner and highly recognized scientist - 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. He received the Weldon Memorial prize and Medal in 2013 for contributions to mathematical biology and was the 2016 recipient of the Charles Branch Award for unparalleled breakthroughs in Brain Research and the Glass Brain Award, a lifetime achievement award in the field of human brain mapping.
During and after his research on schizophrenia and theoretical studies of value-learning, he invented several neurotechnology techniques: statistical parametric mapping (SPM), voxel-based morphometry (VBM) and dynamic causal modelling (DCM).
Currently working on models of functional integration in the human brain and the principles that underlie neuronal interactions.
In his presentation “Me and my Markov blanket” he will show a heuristic proof suggesting that life or biological self-organization is an essential property of any random dynamical system which possesses a Markov blanket.
Topic: "Me and my Markov blanket"
This presentation offers a heuristic proof (and simulations of a primordial soup) suggesting that life—or biological self-organization—is an inevitable and emergent property of any random dynamical system that possesses a Markov blanket. This conclusion is based on the following arguments: if a system can be differentiated from its external milieu, then the system’s internal and external states must be conditionally independent. These independencies induce a Markov blanket that separates internal and external states. Crucially, this equips internal states with an information geometry, pertaining to probabilistic beliefs about something; namely external states. This free energy is the same quantity that is optimized in Bayesian inference and machine learning (where it is known as an evidence lower bound). In short, internal states will appear to model—and act on—their world to preserve their integrity. This leads to a Bayesian mechanics, which can be neatly summarised as self-evidencing. I will try to unpack these ideas using simulations and relate them to predictive processing and sentient behaviour.