title: Towards a Neuronal Gauge Theory.
abstract: Given the amount of knowledge and data accruing in the neurosciences, is it time to formulate a general principle for neuronal dynamics that holds at evolutionary, developmental, and perceptual timescales? In this paper, we propose that the brain (and other self-organised biological systems) can be characterised via the mathematical apparatus of a gauge theory. The picture that emerges from this approach suggests that any biological system (from a neuron to an organism) can be cast as resolving uncertainty about its external milieu, either by changing its internal states or its relationship to the environment. Using formal arguments, we show that a gauge theory for neuronal dynamics--based on approximate Bayesian inference--has the potential to shed new light on phenomena that have thus far eluded a formal description, such as attention and the link between action and perception.
authors.affiliation: Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom.
authors.forename: Biswa
authors.initials: B
authors.lastname: Sengupta
authors.affiliation: Center for Nonlinear Science, University of North Texas, Denton, Texas, United States of America.
authors.forename: Arturo
authors.initials: A
authors.lastname: Tozzi
authors.affiliation: Clinical Neurophysiology, Karolinska University Hospital, Stockholm, Sweden.
authors.forename: Gerald K
authors.initials: GK
authors.lastname: Cooray
authors.affiliation: LINT Laboratory, University of California, Los Angeles, Los Angeles California, United States of America.
authors.forename: Pamela K
authors.initials: PK
authors.lastname: Douglas
authors.affiliation: Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom.
authors.forename: Karl J
authors.initials: KJ
authors.lastname: Friston
mesh_terms: D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D056228:Feedback, Sensory; D008954:Models, Biological; D009474:Neurons / Q000502:physiology*