Allostasis, Interoception, and the Free Energy Principle
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Claude Bernard
DOI: 10.1590/0004-282X20130239 HISTORICAL NOTES Claude Bernard: bicentenary of birth and his main contributions to neurology Claude Bernard: bicentenário do nascimento e suas principais contribuições à neurologia Marleide da Mota Gomes1, Eliasz Engelhardt2 ABSTRACT Claude Bernard (1813-1878) followed two main research paths: the chemical and physiological study of digestion and liver function, along with experimental section of nerves and studies on sympathetic nerves. Curare studies were, for example, of longstanding interest. His profound mental creativity and hand skillfulness, besides methodology quality, directed his experiments and findings, mainly at the Collège de France. His broader and epistemological concerns were carried out at the Sorbonne and later at the Muséum national d’Histoire naturelle. His insight gave clues to define the “ milieu intérieur”, later known as “homeostasis”, and grasp the brain complexity. Bernard followed and surpassed his master François Magendie who also fought against dogmas and laid the foundations of experimental medicine, and its main heinous tool – vivisection. Bernard created the methodological bases of experimental medicine, and collected honors as a renowned researcher. Keywords: Claude Bernard, sympathetic nerves, homeosthasis, epistemology, history of neurosciences. RESUMO Em suas pesquisas, Claude Bernard (1813-1878) seguiu dois caminhos principais: o estudo fisiológico e químico da digestão e da função hepática; a seção experimental de nervos e os estudos sobre nervos simpáticos. Estudos sobre curare, por exemplo, foram de interesse duradouro. Suas profundas criatividade mental e habilidade manual, além da qualidade metodológica, conduziram às suas experiências e descobertas, principalmente no Collège de France. Seus interesses sobre temas epistemológicos mais amplos foram conduzidos na Sorbonne e, posteriormente, no Muséum national d’Histoire naturelle. -
Allostatic Load - a Challenge to Measure Multisystem Physiological Dysregulation
National Centre for Research Methods Working Paper 04/12 Allostatic load - a challenge to measure multisystem physiological dysregulation Sanna Read, London School of Hygiene and Tropical Medicine Emily Grundy, University of Cambridge NCRM Working paper 04/12 Allostatic load – a challenge to measure multisystem physiological dysregulation Sanna Read and Emily Grundy London School of Hygiene and Tropical Medicine and University of Cambridge September 2012 Abstract Allostatic load is a sub-clinical dysregulation state, resulting from the body’s response to stress. Allostatic load accumulates gradually over the life course and affects a number of physiological systems. Measuring multisystem dysregulation and changes in it over time is very challenging. In this paper, we discuss composite measures used to capture allostatic load and the challenges involved in deriving and using these measures. Our focus is on measuring allostatic load in later life. Contents 1 Introduction ........................................................................................................................ 2 2 Allostatic load – a multisystem response to stress ............................................................. 2 3 Measures of allostatic load ................................................................................................. 4 4 Measuring processes over time in allostatic load ............................................................... 6 5 Future directions in measuring allostatic load .................................................................... -
Allostatic Load
Not logged in Talk Contributions Create account Log in Article Talk Read Edit View history Search Wikipedia Allostatic load From Wikipedia, the free encyclopedia Main page This article has multiple issues. Please help improve it or discuss these issues on [hide] Contents the talk page. (Learn how and when to remove these template messages) Featured content This article needs attention from an expert in psychology. (May 2016) Current events This article needs to be updated. (May 2016) Random article Allostatic load is "the wear and tear on the body" which accumulates as an Donate to Wikipedia individual is exposed to repeated or chronic stress. The term was coined by Wikipedia store McEwen and Stellar in 1993. It represents the physiological consequences of chronic exposure to fluctuating or heightened neural or neuroendocrine Interaction response which results from repeated or prolonged chronic stress. Help About Wikipedia Contents [hide] Community portal 1 Regulatory model Recent changes 2 Types Contact page 3 Measurement 4 Relationship to allostasis and homeostasis Tools 5 Reducing risk What links here 6 See also Related changes 7 References The graph represents the effect of increased stress Upload file on the performance of the body. The lower the stress Special pages Regulatory model [ edit ] levels are in the body, the less likely the allostatic load Permanent link model will have a significant effect on the brain and The term allostatic load is "the wear and tear on the body" which accumulates Page information health. Although, an increase in stress levels results in as an individual is exposed to repeated or chronic stress.[1] It was coined by Wikidata item an increase in stress on the brain and the health of McEwen and Stellar in 1993.[2] individuals, making it more likely for the body to have Cite this page The term is part of the regulatory model of allostasis, where the predictive significant effects on homeostasis and cause In other projects regulation or stabilisation of internal sensations in response to stimuli is breakdown of the body systems. -
Early Adversity, Socioemotional Development, and Stress in Urban 1-Year-Old Children
Early Adversity, Socioemotional Development, and Stress in Urban 1-Year-Old Children Frederick B. Palmer, MD1,2, Kanwaljeet J. S. Anand, MBBS, DPhil1,3,4, J. Carolyn Graff, PhD2,5, Laura E. Murphy, EdD2,6, Yanhua Qu, PhD7, Eszter V€olgyi, PhD7, Cynthia R. Rovnaghi, MS1,4, Angela Moore, MPH7, Quynh T. Tran, PhD7, and Frances A. Tylavsky, DrPH7 Objective To determine demographic, maternal, and child factors associated with socioemotional (SE) problems and chronic stress in 1-year-old children. Study design This was a prospective, longitudinal, community-based study, which followed mother-infant dyads (n = 1070; representative of race, education, and income status of Memphis/Shelby County, Tennessee) from midges- tation into early childhood. Child SE development was measured using the Brief Infant-Toddler Social and Emotional Assessment in all 1097 1-year-olds. Chronic stress was assessed by hair cortisol in a subsample of 1-year-olds (n = 297). Multivariate regression models were developed to predict SE problems and hair cortisol levels. Results More black mothers than white mothers reported SE problems in their 1-year-olds (32.9% vs 10.2%; P < .001). In multivariate regression, SE problems in blacks were predicted by lower maternal education, greater parenting stress and maternal psychological distress, and higher cyclothymic personality score. In whites, predictors of SE problems were Medicaid insurance, higher maternal depression score at 1 year, greater parenting stress and maternal psycho- logical distress, higher dysthymic personality score, and male sex. SE problem scores were associated with higher hair cortisol levels (P = .01). Blacks had higher hair cortisol levels than whites (P < .001). -
Deep Active Inference for Autonomous Robot Navigation
Published as a workshop paper at “Bridging AI and Cognitive Science” (ICLR 2020) DEEP ACTIVE INFERENCE FOR AUTONOMOUS ROBOT NAVIGATION Ozan C¸atal, Samuel Wauthier, Tim Verbelen, Cedric De Boom, & Bart Dhoedt IDLab, Department of Information Technology Ghent University –imec Ghent, Belgium [email protected] ABSTRACT Active inference is a theory that underpins the way biological agent’s perceive and act in the real world. At its core, active inference is based on the principle that the brain is an approximate Bayesian inference engine, building an internal gen- erative model to drive agents towards minimal surprise. Although this theory has shown interesting results with grounding in cognitive neuroscience, its application remains limited to simulations with small, predefined sensor and state spaces. In this paper, we leverage recent advances in deep learning to build more complex generative models that can work without a predefined states space. State repre- sentations are learned end-to-end from real-world, high-dimensional sensory data such as camera frames. We also show that these generative models can be used to engage in active inference. To the best of our knowledge this is the first application of deep active inference for a real-world robot navigation task. 1 INTRODUCTION Active inference and the free energy principle underpins the way our brain – and natural agents in general – work. The core idea is that the brain entertains a (generative) model of the world which allows it to learn cause and effect and to predict future sensory observations. It does so by constantly minimising its prediction error or “surprise”, either by updating the generative model, or by inferring actions that will lead to less surprising states. -
Allostasis, Interoception, and the Free Energy Principle
1 Allostasis, interoception, and the free energy principle: 2 Feeling our way forward 3 Andrew W. Corcoran & Jakob Hohwy 4 Cognition & Philosophy Laboratory 5 Monash University 6 Melbourne, Australia 7 8 This material has been accepted for publication in the forthcoming volume entitled The 9 interoceptive mind: From homeostasis to awareness, edited by Manos Tsakiris and Helena De 10 Preester. It has been reprinted by permission of Oxford University Press: 11 https://global.oup.com/academic/product/the-interoceptive-mind-9780198811930?q= 12 interoception&lang=en&cc=gb. For permission to reuse this material, please visit 13 http://www.oup.co.uk/academic/rights/permissions. 14 Abstract 15 Interoceptive processing is commonly understood in terms of the monitoring and representation of the body’s 16 current physiological (i.e. homeostatic) status, with aversive sensory experiences encoding some impending 17 threat to tissue viability. However, claims that homeostasis fails to fully account for the sophisticated regulatory 18 dynamics observed in complex organisms have led some theorists to incorporate predictive (i.e. allostatic) 19 regulatory mechanisms within broader accounts of interoceptive processing. Critically, these frameworks invoke 20 diverse – and potentially mutually inconsistent – interpretations of the role allostasis plays in the scheme of 21 biological regulation. This chapter argues in favour of a moderate, reconciliatory position in which homeostasis 22 and allostasis are conceived as equally vital (but functionally distinct) modes of physiological control. It 23 explores the implications of this interpretation for free energy-based accounts of interoceptive inference, 24 advocating a similarly complementary (and hierarchical) view of homeostatic and allostatic processing. -
Neural Dynamics Under Active Inference: Plausibility and Efficiency
entropy Article Neural Dynamics under Active Inference: Plausibility and Efficiency of Information Processing Lancelot Da Costa 1,2,* , Thomas Parr 2 , Biswa Sengupta 2,3,4 and Karl Friston 2 1 Department of Mathematics, Imperial College London, London SW7 2AZ, UK 2 Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK; [email protected] (T.P.); [email protected] (B.S.); [email protected] (K.F.) 3 Core Machine Learning Group, Zebra AI, London WC2H 8TJ, UK 4 Department of Bioengineering, Imperial College London, London SW7 2AZ, UK * Correspondence: [email protected] Abstract: Active inference is a normative framework for explaining behaviour under the free energy principle—a theory of self-organisation originating in neuroscience. It specifies neuronal dynamics for state-estimation in terms of a descent on (variational) free energy—a measure of the fit between an internal (generative) model and sensory observations. The free energy gradient is a prediction error—plausibly encoded in the average membrane potentials of neuronal populations. Conversely, the expected probability of a state can be expressed in terms of neuronal firing rates. We show that this is consistent with current models of neuronal dynamics and establish face validity by synthesising plausible electrophysiological responses. We then show that these neuronal dynamics approximate natural gradient descent, a well-known optimisation algorithm from information geometry that follows the steepest descent of the objective in information space. We compare the information length of belief updating in both schemes, a measure of the distance travelled in information space that has a Citation: Da Costa, L.; Parr, T.; Sengupta, B.; Friston, K. -
Recent Advances in Thyroid Hormone Regulation: Toward a New Paradigm for Optimal Diagnosis and Treatment
9/30/2019 Recent Advances in Thyroid Hormone Regulation: Toward a New Paradigm for Optimal Diagnosis and Treatment Front Endocrinol (Lausanne). 2017; 8: 364. PMCID: PMC5763098 Published online 2017 Dec 22. doi: 10.3389/fendo.2017.00364 PMID: 29375474 Recent Advances in Thyroid Hormone Regulation: Toward a New Paradigm for Optimal Diagnosis and Treatment Rudolf Hoermann,1,* John E. M. Midgley,2 Rolf Larisch,1 and Johannes W. Dietrich3,4,5 1Department for Nuclear Medicine, Klinikum Lüdenscheid, Lüdenscheid, Germany 2North Lakes Clinical, Ilkley, United Kingdom 3Medical Department I, Endocrinology and Diabetology, Bergmannsheil University Hospitals, Ruhr University of Bochum, Bochum, Germany 4Ruhr Center for Rare Diseases (CeSER), Ruhr University of Bochum, Bochum, Germany 5Ruhr Center for Rare Diseases (CeSER), Witten/Herdecke University, Bochum, Germany Edited by: Noriyuki Koibuchi, Gunma University, Japan Reviewed by: Xuguang Zhu, National Cancer Institute (NIH), United States; Pieter de Lange, Università degli Studi della Campania “Luigi Vanvitelli” Caserta, Italy *Correspondence: Rudolf Hoermann, [email protected] Specialty section: This article was submitted to Thyroid Endocrinology, a section of the journal Frontiers in Endocrinology Homeostasis and allostasis of thyroid function: https://www.frontiersin.org/research-topics/4262/homeostasis- and-allostasis-of-thyroid-function Received 2017 Oct 30; Accepted 2017 Dec 12. Copyright © 2017 Hoermann, Midgley, Larisch and Dietrich. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. -
Treating Thyroid Disease: a Natural Approach to Healing Hashimoto's
Treating Thyroid Disease: A Natural Approach to Healing Hashimoto’s Melissa Lea-Foster Rietz, FNP-BC, BC-ADM, RYT-200 Presbyterian Medical Services Farmington, NM [email protected] Professional Disclosures I have no personal or professional affiliation with any of the resources listed in this presentation, and will receive no monetary gain or professional advancement from this lecture. Talk Objectives • Define hypothyroidism and Hashimoto’s. • Discuss various tests used to identify thyroid disease and when to treat based on patient symptoms • Discuss potential causes and identify environmental factors that contribute to disease • Describe how the gut (food sensitivities) and the adrenals (chronic stress) are connected to Hashimoto’s and how we as practitioners can work to educate patients on prevention before the need for treatment • How the use of adaptogens can enhance the treatment of Hashimoto’s and identify herbs that are showing promise in the research. • How to use food, exercise, and relaxation to improve patient outcomes. Named for Hakuro Hashimoto, a physician working in Europe in the early 1900’s. Hashimoto’s was the first autoimmune disease to be recognized in the scientific literature. It is estimated that one in five people suffer from an autoimmune disease and the numbers continue to rise. Women are more likely than men to develop an autoimmune disease, and it is believed that 75% of individuals with an autoimmune disease are female. Thyroid autoimmune disease is the most common form, and affects 7-8% of the population in the United States. Case Study Ms. R is a 30-year-old female, mother of three, who states that after the birth of her last child two years ago she has felt the following: • Loss of energy • Difficulty losing weight despite habitual eating pattern • Hair loss • Irregular menses • Joints that ache throughout the day • A general sense of sadness • Cold Intolerance • Joint and Muscle Pain • Constipation • Irregular menstruation • Slowed Heart Rate What tests would you run on Ms. -
15 | Variational Inference
15 j Variational inference 15.1 Foundations Variational inference is a statistical inference framework for probabilistic models that comprise unobserv- able random variables. Its general starting point is a joint probability density function over observable random variables y and unobservable random variables #, p(y; #) = p(#)p(yj#); (15.1) where p(#) is usually referred to as the prior density and p(yj#) as the likelihood. Given an observed value of y, the first aim of variational inference is to determine the conditional density of # given the observed value of y, referred to as the posterior density. The second aim of variational inference is to evaluate the marginal density of the observed data, or, equivalently, its logarithm Z ln p(y) = ln p(y; #) d#: (15.2) Eq. (15.2) is commonly referred to as the log marginal likelihood or log model evidence. The log model evidence allows for comparing different models in their plausibility to explain observed data. In the variational inference framework it is not the log model evidence itself which is evaluated, but rather a lower bound approximation to it. This is due to the fact that if a model comprises many unobservable variables # the integration of the right-hand side of (15.2) can become analytically burdensome or even intractable. To nevertheless achieve its two aims, variational inference in effect replaces an integration problem with an optimization problem. To this end, variational inference exploits a set of information theoretic quantities as introduced in Chapter 11 and below. Specifically, the following log model evidence decomposition forms the core of the variational inference approach (Figure 15.1): ln p(y) = F (q(#)) + KL(q(#)kp(#jy)): (15.3) In eq. -
D4d78cb0277361f5ccf9036396b
critical terms for media studies CRITICAL TERMS FOR MEDIA STUDIES Edited by w.j.t. mitchell and mark b.n. hansen the university of chicago press Chicago and London The University of Chicago Press, Chicago 60637 The University of Chicago Press, Ltd., London © 2010 by The University of Chicago All rights reserved. Published 2010 Printed in the United States of America 18 17 16 15 14 13 12 11 10 1 2 3 4 5 isbn- 13: 978- 0- 226- 53254- 7 (cloth) isbn- 10: 0- 226- 53254- 2 (cloth) isbn- 13: 978- 0- 226- 53255- 4 (paper) isbn- 10: 0- 226- 53255- 0 (paper) Library of Congress Cataloging-in-Publication Data Critical terms for media studies / edited by W. J. T. Mitchell and Mark Hansen. p. cm. Includes index. isbn-13: 978-0-226-53254-7 (cloth : alk. paper) isbn-10: 0-226-53254-2 (cloth : alk. paper) isbn-13: 978-0-226-53255-4 (pbk. : alk. paper) isbn-10: 0-226-53255-0 (pbk. : alk. paper) 1. Literature and technology. 2. Art and technology. 3. Technology— Philosophy. 4. Digital media. 5. Mass media. 6. Image (Philosophy). I. Mitchell, W. J. T. (William John Th omas), 1942– II. Hansen, Mark B. N. (Mark Boris Nicola), 1965– pn56.t37c75 2010 302.23—dc22 2009030841 The paper used in this publication meets the minimum requirements of the American National Standard for Information Sciences—Permanence of Paper for Printed Library Materials, ansi z39.48- 1992. Contents Introduction * W. J. T. Mitchell and Mark B. N. Hansen vii aesthetics Art * Johanna Drucker 3 Body * Bernadette Wegenstein 19 Image * W. -
Developmental Reasoning and Planning with Robot Through Enactive Interaction with Human Maxime Petit
Developmental reasoning and planning with robot through enactive interaction with human Maxime Petit To cite this version: Maxime Petit. Developmental reasoning and planning with robot through enactive interaction with human. Automatic. Université Claude Bernard - Lyon I, 2014. English. NNT : 2014LYO10037. tel-01015288 HAL Id: tel-01015288 https://tel.archives-ouvertes.fr/tel-01015288 Submitted on 26 Jun 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. N˚d’ordre 37 - 2014 Année 2013 THESE DE L’UNIVERSITE DE LYON présentée devant L’UNIVERSITE CLAUDE BERNARD LYON 1 Ecole Doctorale Neurosciences et Cognition pour l’obtention du Diplôme de Thèse (arrété du 7 août 2006) Discipline : SCIENCES COGNITIVES Option : INFORMATIQUE présentée et soutenue publiquement le 6 Mars 2014 par Maxime Raisonnement et Planification Développementale d’un Robot via une Interaction Enactive avec un Humain Developmental Reasoning and Planning with Robot through Enactive Interaction with Human dirigée par Peter F. Dominey devant le jury composé de : Pr. Rémi Gervais Président du jury Pr. Giorgio Metta Rapporteur Pr. Philippe Gaussier Rapporteur Pr. Chrystopher Nehaniv Examinateur Dr. Jean-Christophe Baillie Examinateur Dr. Peter Ford Dominey Directeur de thèse 2 Stem-Cell and Brain Research Insti- École doctorale Neurosciences et Cog- tute, INSERM U846 nition (ED 476 - NSCo) 18, avenue Doyen Lepine UCBL - Lyon 1 - Campus de Gerland 6975 Bron Cedex 50, avenue Tony Garnier 69366 Lyon Cedex 07 Pour mon père.