15
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Towards a Neuronal Gauge Theory

      other

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Abstract

          This Essay presents a formalism that not only provides a quantitative framework for modelling neural activity but also shows that neuronal dynamics across scales are described by the same principle.

          Related collections

          Most cited references15

          • Record: found
          • Abstract: found
          • Article: not found

          The Helmholtz machine.

          Discovering the structure inherent in a set of patterns is a fundamental aim of statistical inference or learning. One fruitful approach is to build a parameterized stochastic generative model, independent draws from which are likely to produce the patterns. For all but the simplest generative models, each pattern can be generated in exponentially many ways. It is thus intractable to adjust the parameters to maximize the probability of the observed patterns. We describe a way of finessing this combinatorial explosion by maximizing an easily computed lower bound on the probability of the observations. Our method can be viewed as a form of hierarchical self-supervised learning that may relate to the function of bottom-up and top-down cortical processing pathways.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Object perception as Bayesian inference.

            We perceive the shapes and material properties of objects quickly and reliably despite the complexity and objective ambiguities of natural images. Typical images are highly complex because they consist of many objects embedded in background clutter. Moreover, the image features of an object are extremely variable and ambiguous owing to the effects of projection, occlusion, background clutter, and illumination. The very success of everyday vision implies neural mechanisms, yet to be understood, that discount irrelevant information and organize ambiguous or noisy local image features into objects and surfaces. Recent work in Bayesian theories of visual perception has shown how complexity may be managed and ambiguity resolved through the task-dependent, probabilistic integration of prior object knowledge with image features.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Life as we know it

              This paper presents a heuristic proof (and simulations of a primordial soup) suggesting that life—or biological self-organization—is an inevitable and emergent property of any (ergodic) random dynamical system that possesses a Markov blanket. This conclusion is based on the following arguments: if the coupling among an ensemble of dynamical systems is mediated by short-range forces, then the states of remote systems must be conditionally independent. These independencies induce a Markov blanket that separates internal and external states in a statistical sense. The existence of a Markov blanket means that internal states will appear to minimize a free energy functional of the states of their Markov blanket. Crucially, this is the same quantity that is optimized in Bayesian inference. Therefore, the internal states (and their blanket) will appear to engage in active Bayesian inference. In other words, they will appear to model—and act on—their world to preserve their functional and structural integrity, leading to homoeostasis and a simple form of autopoiesis.
                Bookmark

                Author and article information

                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                8 March 2016
                March 2016
                8 March 2016
                : 14
                : 3
                : e1002400
                Affiliations
                [1 ]Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
                [2 ]Center for Nonlinear Science, University of North Texas, Denton, Texas, United States of America
                [3 ]Clinical Neurophysiology, Karolinska University Hospital, Stockholm, Sweden
                [4 ]LINT Laboratory, University of California, Los Angeles, Los Angeles California, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Article
                PBIOLOGY-D-15-02155
                10.1371/journal.pbio.1002400
                4783098
                26953636
                d4e2917b-d3de-4afe-84a7-c8cbcd254127
                © 2016 Sengupta et al

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

                History
                Page count
                Figures: 1, Tables: 0, Pages: 12
                Funding
                BS and KJF are supported by the Wellcome Trust (088130/Z/09/Z). BS is thankful to the Mathematical Biosciences Institute for hosting him, and facilitating communication with experts working on the symmetry perspective of dynamical systems. We thank John Ashburner for his comments on an initial draft of this paper. GKC is supported by a postdoctoral fellowship from the Swedish Brain Foundation (Hjärnfonden). PD is funded by the Klingenstein Foundation, and the Keck Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Essay
                Physical Sciences
                Physics
                Thermodynamics
                Free Energy
                Physical Sciences
                Mathematics
                Topology
                Manifolds
                Physical Sciences
                Mathematics
                Geometry
                Symmetry
                Biology and Life Sciences
                Neuroscience
                Sensory Perception
                Biology and Life Sciences
                Psychology
                Sensory Perception
                Social Sciences
                Psychology
                Sensory Perception
                Physical Sciences
                Mathematics
                Probability Theory
                Probability Distribution
                Physical Sciences
                Mathematics
                Geometry
                Differential Geometry
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Psychology
                Attention
                Biology and Life Sciences
                Psychology
                Cognitive Psychology
                Attention
                Social Sciences
                Psychology
                Cognitive Psychology
                Attention
                Biology and Life Sciences
                Anatomy
                Nervous System
                Medicine and Health Sciences
                Anatomy
                Nervous System

                Life sciences
                Life sciences

                Comments

                Comment on this article