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The Graphical Brain: Belief Propagation and Active Inference

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The Graphical Brain: Belief Propagation and Active Inference RESEARCH The graphical brain: Belief propagation and active inference 1 1 2,3 Karl J. Friston , Thomas Parr , and Bert de Vries 1Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, United Kingdom 2Eindhoven University of Technology, Department of Electrical Engineering, Eindhoven, The Netherlands 3 GN Hearing, Eindhoven, The Netherlands Keywords: Bayesian, Neuronal, Connectivity, Factor graphs, Free energy, Belief propagation, Message passing an open access journal ABSTRACT This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference—and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models. Crucially, these models can entertain both discrete and continuous states, leading to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to elucidate the requisite message passing in terms of its form and scheduling. To accommodate mixed generative models (of discrete and continuous states), one also has to consider link nodes or factors that enable discrete and continuous representations to talk to each other. When mapping the implicit computational architecture onto neuronal connectivity, several interesting features Citation: Friston, K. J., Parr, T., & de Vries, B. (2017). The graphical brain: emerge. For example, Bayesian model averaging and comparison, which link discrete and Belief propagation and active inference. Network Neuroscience, continuous states, may be implemented in thalamocortical loops. These and other 1(4), 381–414. https://doi.org/10.1162/ considerations speak to a computational connectome that is inherently state dependent and netn_a_00018 self-organizing in ways that yield to a principled (variational) account. We conclude with DOI: simulations of reading that illustrate the implicit neuronal message passing, with a special https://doi.org/10.1162/netn_a_00018 focus on how discrete (semantic) representations inform, and are informed by, continuous Supporting Information: (visual) sampling of the sensorium. http://www.fil.ion.ucl.ac.uk/spm/ Received: 1 February 2017 AUTHOR SUMMARY Accepted: 10 May 2017 This paper considers functional integration in the brain from a computational perspective. Competing Interests: The authors have declared that no competing interests We ask what sort of neuronal message passing is mandated by active inference—and what exist. implications this has for context-sensitive connectivity at microscopic and macroscopic Corresponding Author: levels. In particular, we formulate neuronal processing as belief propagation under deep Karl Friston generative models that can entertain both discrete and continuous states. This leads to distinct [email protected] schemes for belief updating that play out on the same (neuronal) architecture. Technically, Handling Editor: we use Forney (normal) factor graphs to characterize the requisite message passing, and link Randy McIntosh this formal characterization to canonical microcircuits and extrinsic connectivity in the brain. Copyright: © 2017 Massachusetts Institute of Technology Published under a Creative Commons INTRODUCTION Attribution 4.0 International (CC BY 4.0) license This paper attempts to describe functional integration in the brain in terms of neuronal com- putations. We start by asking what the brain does, to see how far the implicit constraints on The MIT Press Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/netn_a_00018 by guest on 03 October 2021 The graphical brain neuronal message passing can take us. In particular, we assume that the brain engages in some form of (Bayesian) inference—and can therefore be described as maximizing Bayesian model evidence (Clark, 2013; Friston, Kilner, & Harrison, 2006; Hohwy, 2016; Mumford, 1992). Generative model: This implies that the brain embodies a generative model, for which it tries to gather the great- Or forward model; a probabilistic est evidence. On this view, to understand functional integration is to understand the form mapping from causes to observed of the generative model and how it is used to make inferences about sensory data that are consequences (data). It is usually sampled actively from the world. Happily, there is an enormous amount known about the specified in terms of the likelihood of getting some data given their causes various schemes that can implement this form of (Bayesian) inference, thereby offering the (parameters of a model) and priors possibility of developing a process theory (i.e., neuronally plausible scheme) that implements on the parameters. the normative principle of self-evidencing (Hohwy, 2016). In brief, this (rather long) paper tries to integrate three themes to provide a rounded per- spective on message passing or belief propagation in the brain. These themes include (a) the formal basis of belief propagation, from the perspective of the Bayesian brain and active inference; (b) the biological substrates of the implicit message passing; and (c) how discrete Generalized coordinates of motion: representations (e.g., semantics) might talk to representations of continuous quantities (e.g., Cover the value of a variable, its motion, acceleration, jerk, and visual contrast luminance). Technically, the key contributions are twofold: first, the derivation higher orders of motion. A point in of belief propagation and Bayesian filtering in generalized coordinates of motion, under the generalized coordinates corresponds framework afforded by factor graphs. This derivation highlights the similarities between rep- to a path or trajectory over time. resentations of trajectories over future time points, in discrete models, and the representation of trajectories in generalized coordinates of motion in continuous models. Second, we de- Factor graph: scribed a fairly generic way in which discrete and continuous representations can be linked A bipartite graph (where two distinct sets of nodes are connected by through Bayesian model selection and averaging. To leverage these technical developments, edges) representing the factorization for an understanding of brain function, we highlight the constraints they offer on the structure of a function, usually a probability and dynamics of neuronal message passing, using coarse-grained evidence from anatomy and distribution function. Formulating neurophysiology. Finally, the nature of this message passing is illustrated using simulations of a Bayesian network or model as a pictographic reading. factor graph enables the efficient computation of marginal distributions, through the In what follows, we use graphical representations to characterize message passing under sum-product algorithm. deep (hierarchical) generative models that might be used by the brain. We use three sorts of graphs to emphasize the form of generative models, the nature of Bayesian belief updating, and how this might be accomplished in neuronal circuits—both at the level of macroscopic cortical hierarchies and at the more detailed level of canonical microcircuits. The three sorts of Bayesian network: graphs include Bayesian networks or dependency graphs (MacKay, 1995; Pearl, 1988), where A Bayes network, model, or belief nodes correspond to unknown variables that have to be inferred and the edges denote depen- network is a probabilistic graphical dencies among these (random) variables. This provides a concise description of how (e.g., model that represents a set of sensory) data are generated. To highlight the requisite message passing and computational random variables and their conditional dependencies via a architecture, we will use Forney or normal style factor graphs (Loeliger, 2002). In Forney directed acyclic graph. factor graphs, the nodes now represent local functions or factors of a probability distribu- tion over the random variables, while edges come to represent the variables per se (or more exactly a probability distribution over those variables). Finally, we will use neural networks or circuits where the nodes are constituted by the sufficient statistics of unknown variables and other auxiliary variables, such as prediction errors. The edges in these graphs denote an ex- change of (functions of) sufficient statistics—of the sort one can interpret in terms of neuronal (axonal) connections. Crucially, these graphical representations are formally equivalent in the sense that any Bayesian network can be expressed as a factor graph. And any message passing on a factor graph can be depicted as a neural network. However, as we will see later, the various graphical formulations offer different perspectives on belief updating or propagation. We will leverage these perspectives to work from a purely normative theory (based upon the Network Neuroscience 382 Downloaded from http://www.mitpressjournals.org/doi/pdf/10.1162/netn_a_00018 by guest on 03 October 2021 The graphical brain Free energy: maximization of Bayesian model evidence or minimization of variational free energy) towards An information theory measure that a process theory (based upon belief propagation and the attractors of dynamical systems). bounds (is greater than) the surprise on sampling some data, given a In this paper, we use Forney factor graphs for purely didactic purposes; namely, to illustrate generative
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