Living systems face both environmental complexity and limited access to free-energy resources. Survival under these conditions requires a control system that can activate, or deploy, available perception and action resources in a context specific way. We show here that when systems are described as executing active inference driven by the free-energy principle (and hence can be considered Bayesian prediction-error minimizers), their control flow systems can always be represented as tensor networks (TNs). We show how TNs as control systems can be implmented within the general framework of quantum topological neural networks, and discuss the implications of these results for modeling biological systems at multiple scales.
This paper describes a Bayesian method for three-dimensional registration of brain images. A finite element approach is used to obtain a maximum a posteriori estimate of the deformation field at every voxel of a template volume. The priors used by the MAP estimate penalize unlikely deformations and enforce a continuous one-to-one mapping. The deformations are assumed to have some form of symmetry, in that priors describing the probability distribution of the deformations should be identical to those for the inverses (i.e., warping brain A to brain B should not be different probablistically from warping B to A). A gradient descent algorithm is presented for estimating the optimum deformations.
Event Abstract Back to Event Adaptation: A functionalist perspective Karl J. Friston1* 1 Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom We will ague that adaption (as a neurophysiological phenomenon) has an essential role from a functionalist perspective and try to understand it as part of perceptual inference in the brain. In brief, purely theoretical considerations suggest that perception has to optimize two things; the cause of a sensory inputs and the precision of that input. This is a bit like computing a t-test; using the difference in group means and standard error (inverse precision). We will suggest that adaption reflects the optimization of post-synaptic sensitivity to encode the precision of sensory information, while plastic changes in synaptic connections represent an optimization of the internal model of what caused that input. Conference: MMN 09 Fifth Conference on Mismatch Negativity (MMN) and its Clinical and Scientific Applications, Budapest, Hungary, 4 Apr - 7 Apr, 2009. Presentation Type: Oral Presentation Topic: Workshop 2: The role of adaptation in deviance detection Citation: Friston KJ (2009). Adaptation: A functionalist perspective. Conference Abstract: MMN 09 Fifth Conference on Mismatch Negativity (MMN) and its Clinical and Scientific Applications. doi: 10.3389/conf.neuro.09.2009.05.013 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 19 Mar 2009; Published Online: 19 Mar 2009. * Correspondence: Karl J Friston, Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom, k.friston@ucl.ac.uk Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Karl J Friston Google Karl J Friston Google Scholar Karl J Friston PubMed Karl J Friston Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.
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Introduction The value of face coverings in the pandemic has been disputed. Dynamic causal modelling (DCM) allows one to test hypotheses about the nature of viral spread and effectiveness of interventions. Our aim was to quantify the impact of face coverings on viral transmission and deaths associated with COVID-19 between 2020 and 2023 in the UK. Methods A DCM of the COVID-19 outbreak in the UK was used. It combines conventional (ie, Susceptible Exposed Infectious Recovered (SEIR)) epidemiological and sociobehavioural models to simultaneously fit measures of prevalence, clinical data, vaccine use and fluctuations in mobility. The model was augmented to include an effect of reported use of face coverings within the past week by adults in reducing community transmission, which was then suppressed to see what might have happened in their absence. Results We found face coverings by adults reduced COVID-19 transmission by 5% in adults (90% CI 4.6% to 5.6%) and the elderly (90% CI 4.8% to 5.6%), but only by 2.3% in children (90% CI 1.5% to 3.4%) and had negligible effects in younger adults (0.4%, 90% CI 0.2% to 0.8%). This effect on transmission had a large impact on mortality, with an estimated saving of 102 000 (90% CI range 40 000 to 164 000) lives over the 30-month study period, suggesting a 50% reduction in mortality. Conclusions Modelling the impact of widely used face coverings during the pandemic revealed a quantitatively small (age-dependent) impact on community transmission that translates into a marked public health impact, as estimated by the prevention of deaths.
This paper considers the problem of sensorimotor delays in the optimal control of (smooth) eye movements under uncertainty. Specifically, we consider delays in the visuo-oculomotor loop and their implications for active inference. Active inference uses a generalisation of Kalman filtering to provide Bayes optimal estimates of hidden states and action in generalised coordinates of motion. Representing hidden states in generalised coordinates provides a simple way of compensating for both sensory and oculomotor delays. The efficacy of this scheme is illustrated using neuronal simulations of pursuit initiation responses, with and without compensation. We then consider an extension of the generative model to simulate smooth pursuit eye movements-in which the visuo-oculomotor system believes both the target and its centre of gaze are attracted to a (hidden) point moving in the visual field. Finally, the generative model is equipped with a hierarchical structure, so that it can recognise and remember unseen (occluded) trajectories and emit anticipatory responses. These simulations speak to a straightforward and neurobiologically plausible solution to the generic problem of integrating information from different sources with different temporal delays and the particular difficulties encountered when a system-like the oculomotor system-tries to control its environment with delayed signals.
R ecently, there have been several concerted international efforts—the BRAIN Initiative, the European Human Brain Project, and the Human Connectome Project, to name a few—that hope to revolutionize our under standing of the connected brain. During the past two de cades, functional neuroimaging has emerged as the predominant technique in systems neuroscience. This is foreshadowed by an everincreasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. In this article, we summarize pedagogically the (deep) history of brain mapping. We highlight the theoretical advances made in the (dynamic) causal modeling of brain function, which may have escaped the wider audience of this article, and provide a brief overview of recent developments and interesting clinical applications. We hope that this arti cle engages the signal processing community by showcasing the inherently multidisciplinary nature of this important topic and the intriguing questions that are being addressed.
At its simplest, voxel-based morphometry (VBM) involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing high-resolution images from all the subjects in the study into the same stereotactic space. This is followed by segmenting the gray matter from the spatially normalized images and smoothing the gray-matter segments. Voxel-wise parametric statistical tests which compare the smoothed gray-matter images from the two groups are performed. Corrections for multiple comparisons are made using the theory of Gaussian random fields. This paper describes the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with nonuniformity artifact. We provide evaluations of the assumptions that underpin the method, including the accuracy of the segmentation and the assumptions made about the statistical distribution of the data.
Recent advances in theoretical biology suggest that key definitions of basal cognition and sentient behavior may arise as emergent properties of in vitro cell cultures and neuronal networks. Such neuronal networks reorganize activity to demonstrate structured behaviors when embodied in structured information landscapes. In this article, we characterize this kind of self-organization through the lens of the free energy principle, that is, as self-evidencing. We do this by first discussing the definitions of reactive and sentient behavior in the setting of active inference, which describes the behavior of agents that model the consequences of their actions. We then introduce a formal account of intentional behavior that describes agents as driven by a preferred end point or goal in latent state-spaces. We then investigate these forms of (reactive, sentient, and intentional) behavior using simulations. First, we simulate the in vitro experiments, in which neuronal cultures modulated activity to improve gameplay in a simplified version of Pong by implementing nested, free energy minimizing processes. The simulations are then used to deconstruct the ensuing predictive behavior, leading to the distinction between merely reactive, sentient, and intentional behavior with the latter formalized in terms of inductive inference. This distinction is further studied using simple machine learning benchmarks (navigation in a grid world and the Tower of Hanoi problem) that show how quickly and efficiently adaptive behavior emerges under an inductive form of active inference.
Neuroimaging studies of psychedelics have advanced our understanding of hierarchical brain organisation and the mechanisms underlying their subjective and therapeutic effects. The primary mechanism of action of classic psychedelics is binding to serotonergic 5HT2A receptors. Agonist activity at these receptors leads to neuromodulatory changes in synaptic efficacy that can have a profound effect on hierarchical message passing in the brain. Here, we review the cognitive and neuroimaging evidence for the effects of psychedelics; in particular, their influence on selfhood and subject-object boundaries—known as ego dissolution—surmised to underwrite their subjective and therapeutic effects. Agonist of 5HT2A receptors, located at the apex of the cortical hierarchy may have a particularly powerful effect on sentience and consciousness. These effects can endure well after the pharmacological half life, suggesting that psychedelics may have long-term effects on neural plasticity – that may play a role in their therapeutic efficacy. Psychologically, this may be accompanied by a surrender of ego resistance that increases the repertoire of perceptual hypotheses, including those that undergird selfhood. We consider the interaction between serotonergic neuromodulation and sentience through the lens of hierarchical predictive coding, which speaks to the value of psychedelics in understanding how we make sense of the world—and specific predictions about effective connectivity in cortical hierarchies that can be tested using functional neuroimaging.
