Respiration, Neural Oscillations, and the Free Energy Principle
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Taste and Smell Disorders in Clinical Neurology
TASTE AND SMELL DISORDERS IN CLINICAL NEUROLOGY OUTLINE A. Anatomy and Physiology of the Taste and Smell System B. Quantifying Chemosensory Disturbances C. Common Neurological and Medical Disorders causing Primary Smell Impairment with Secondary Loss of Food Flavors a. Post Traumatic Anosmia b. Medications (prescribed & over the counter) c. Alcohol Abuse d. Neurodegenerative Disorders e. Multiple Sclerosis f. Migraine g. Chronic Medical Disorders (liver and kidney disease, thyroid deficiency, Diabetes). D. Common Neurological and Medical Disorders Causing a Primary Taste disorder with usually Normal Olfactory Function. a. Medications (prescribed and over the counter), b. Toxins (smoking and Radiation Treatments) c. Chronic medical Disorders ( Liver and Kidney Disease, Hypothyroidism, GERD, Diabetes,) d. Neurological Disorders( Bell’s Palsy, Stroke, MS,) e. Intubation during an emergency or for general anesthesia. E. Abnormal Smells and Tastes (Dysosmia and Dysgeusia): Diagnosis and Treatment F. Morbidity of Smell and Taste Impairment. G. Treatment of Smell and Taste Impairment (Education, Counseling ,Changes in Food Preparation) H. Role of Smell Testing in the Diagnosis of Neurodegenerative Disorders 1 BACKGROUND Disorders of taste and smell play a very important role in many neurological conditions such as; head trauma, facial and trigeminal nerve impairment, and many neurodegenerative disorders such as Alzheimer’s, Parkinson Disorders, Lewy Body Disease and Frontal Temporal Dementia. Impaired smell and taste impairs quality of life such as loss of food enjoyment, weight loss or weight gain, decreased appetite and safety concerns such as inability to smell smoke, gas, spoiled food and one’s body odor. Dysosmia and Dysgeusia are very unpleasant disorders that often accompany smell and taste impairments. -
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. -
Review Article Olfactory Reference Syndrome
British Journal of Pharmaceutical and Medical Research Vol.04, Issue 01, Pg.1617-1625, January-February 2019 Available Online at http://www.bjpmr.org BRITISH JOURNAL OF PHARMACEUTICAL AND MEDICAL RESEARCH Review ISSN:2456-9836 ICV: 60.37 Article Murat Eren Özen, Murat Aydin Olfactory Reference Syndrome: A Separate Disorder Or Part Of A Spectrum 1Psychiatrist, Department of Psychiatry, Private Adana Hospital, Adana Büyük şehir Belediyesi kar şısı, No:23, Seyhan-Adana- Türkiye. 2Private Dental Clinics, Gazipa şa bulv. Emre apt n:6 (kitapsan kar şısı) k:2 d:5 Adana- Türkiye. http://drmurataydin.com ARTICLE INFO ABSTRACT This article provides a narrative review of the literature on olfactory reference syndrome Article History: Received on (ORS) to address issues focusing on its clinical features. Similarities and/or differences with other psychiatric disorders such as obsessive-compulsive spectrum disorders, social anxiety 10 th Jan, 2019 Peer Reviewed disorder (including a cultural syndrome; taijin kyofusho), somatoform disorders and hypochondriasis, delusional disorder are discussed. ORS is related to a symptom of taijin on 24 th Jan, 2019 Revised kyofusho (e.g. jikoshu-kyofu variant of taijin kyofusho) Although recognition of this syndromes more than a century provide consistent descriptions of its clinical features, the on 17 th Feb, 2019 Published limited data on this topic make it difficult to form a specific diagnostic criteria. The core on 24 th Feb, symptom of the patients with ORS is preoccupation with the belief that one emits a foul or 2019 offensive body odor, which is not perceived by others. Studies on ORS reveal some limitations. -
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. -
Active Inference Body Perception and Action for Humanoid Robots
Active inference body perception and action for humanoid robots Guillermo Oliver1, Pablo Lanillos1;2, Gordon Cheng1 Abstract—Providing artificial agents with the same computa- Body perception tional models of biological systems is a way to understand how intelligent behaviours may emerge. We present an active inference body perception and action model working for the first time in a humanoid robot. The model relies on the free energy principle proposed for the brain, where both perception and action goal is to minimise the prediction error through gradient descent on the variational free energy bound. The body state (latent variable) is inferred by minimising the difference between the observed (visual and proprioceptive) sensor values and the predicted ones. Simultaneously, the action makes sensory data sampling to better Action correspond to the prediction made by the inner model. We formalised and implemented the algorithm on the iCub robot and tested in 2D and 3D visual spaces for online adaptation to visual changes, sensory noise and discrepancies between the Fig. 1. Body perception and action via active inference. The robot infers model and the real robot. We also compared our approach with its body (e.g., joint angles) by minimising the prediction error: discrepancy classical inverse kinematics in a reaching task, analysing the between the sensors (visual sv and joint sp) and their expected values (gv(µ) suitability of such a neuroscience-inspired approach for real- and gp(µ)). In the presence of error it changes the perception of its body µ world interaction. The algorithm gave the robot adaptive body and generates an action a to reduce this discrepancy. -
A Free Energy Principle for Biological Systems
Entropy 2012, 14, 2100-2121; doi:10.3390/e14112100 OPEN ACCESS entropy ISSN 1099-4300 www.mdpi.com/journal/entropy Article A Free Energy Principle for Biological Systems Friston Karl The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, Queen Square, London, WC1N 3BG, UK; E-Mail: [email protected]; Tel.: +44 (0)203 448 4347; Fax: +44 (0)207 813 1445 Received: 17 August 2012; in revised form: 1 October 2012 / Accepted: 25 October 2012 / Published: 31 October 2012 Abstract: This paper describes a free energy principle that tries to explain the ability of biological systems to resist a natural tendency to disorder. It appeals to circular causality of the sort found in synergetic formulations of self-organization (e.g., the slaving principle) and models of coupled dynamical systems, using nonlinear Fokker Planck equations. Here, circular causality is induced by separating the states of a random dynamical system into external and internal states, where external states are subject to random fluctuations and internal states are not. This reduces the problem to finding some (deterministic) dynamics of the internal states that ensure the system visits a limited number of external states; in other words, the measure of its (random) attracting set, or the Shannon entropy of the external states is small. We motivate a solution using a principle of least action based on variational free energy (from statistical physics) and establish the conditions under which it is formally equivalent to the information bottleneck method. This approach has proved useful in understanding the functional architecture of the brain. The generality of variational free energy minimisation and corresponding information theoretic formulations may speak to interesting applications beyond the neurosciences; e.g., in molecular or evolutionary biology. -
A Free Energy Principle for a Particular Physics
A FREE ENERGY PRINCIPLE FOR A PARTICULAR PHYSICS Karl Friston The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK WC1N 3AR. Email: [email protected] (This work is under consideration for publication by The MIT Press) Abstract This monograph attempts a theory of every ‘thing’ that can be distinguished from other ‘things’ in a statistical sense. The ensuing statistical independencies, mediated by Markov blankets, speak to a recursive composition of ensembles (of things) at increasingly higher spatiotemporal scales. This decomposition provides a description of small things; e.g., quantum mechanics – via the Schrödinger equation, ensembles of small things – via statistical mechanics and related fluctuation theorems, through to big things – via classical mechanics. These descriptions are complemented with a Bayesian mechanics for autonomous or active things. Although this work provides a formulation of every ‘thing’, its main contribution is to examine the implications of Markov blankets for self- organisation to nonequilibrium steady-state. In brief, we recover an information geometry and accompanying free energy principle that allows one to interpret the internal states of something as representing or making inferences about its external states. The ensuing Bayesian mechanics is compatible with quantum, statistical and classical mechanics and may offer a formal description of lifelike particles. Key words: self-organisation; nonequilibrium steady-state; active inference; active particles; free -
The Free-Energy Principle: a Rough Guide to the Brain?
Opinion The free-energy principle: a rough guide to the brain? Karl Friston The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK This article reviews a free-energy formulation that The motivation for the free-energy principle is again advances Helmholtz’s agenda to find principles of brain simple but fundamental. It rests upon the fact that self- function based on conservation laws and neuronal organising biological agents resist a tendency to disorder energy. It rests on advances in statistical physics, theor- and therefore minimize the entropy of their sensory states etical biology and machine learning to explain a remark- [4]. Under ergodic assumptions, this entropy is: able range of facts about brain structure and function. We Z could have just scratched the surface of what this formu- HðyÞ¼ pðyjmÞln pðyjmÞdy lation offers; for example, it is becoming clear that the ZT Bayesian brain is just one facet of the free-energy principle 1 and that perception is an inevitable consequence of active ¼ lim À ln pðyjmÞdt (Equation 1) T !1T exchange with the environment. Furthermore, one can 0 see easily how constructs like memory, attention, value, See Box 1 for an explanation of the variables and Ref. [5] reinforcement and salience might disclose their simple for details. This equation (Equation 1) means that mini- relationships within this framework. Introduction The free-energy (see Glossary) principle is a simple pos- Glossary tulate with complicated implications. It says that any [Kullback-Leibler] divergence: information divergence, information gain, cross adaptive change in the brain will minimize free-energy. -
The Free-Energy Principle: a Unified Brain Theory?
REVIEWS The free-energy principle: a unified brain theory? Karl Friston Abstract | A free-energy principle has been proposed recently that accounts for action, perception and learning. This Review looks at some key brain theories in the biological (for example, neural Darwinism) and physical (for example, information theory and optimal control theory) sciences from the free-energy perspective. Crucially, one key theme runs through each of these theories — optimization. Furthermore, if we look closely at what is optimized, the same quantity keeps emerging, namely value (expected reward, expected utility) or its complement, surprise (prediction error, expected cost). This is the quantity that is optimized under the free-energy principle, which suggests that several global brain theories might be unified within a free-energy framework. Free energy Despite the wealth of empirical data in neuroscience, Motivation: resisting a tendency to disorder. The An information theory measure there are relatively few global theories about how the defining characteristic of biological systems is that that bounds or limits (by being brain works. A recently proposed free-energy principle they maintain their states and form in the face of a greater than) the surprise on for adaptive systems tries to provide a unified account constantly changing environment3–6. From the point sampling some data, given a of action, perception and learning. Although this prin- of view of the brain, the environment includes both generative model. ciple has been portrayed as a unified brain theory1, its the external and the internal milieu. This maintenance Homeostasis capacity to unify different perspectives on brain function of order is seen at many levels and distinguishes bio- The process whereby an open has yet to be established. -
Arxiv:1709.02341V5 [Q-Bio.NC] 11 Oct 2018
DEEP ACTIVE INFERENCE KAI UELTZHOFFER¨ Abstract. This work combines the free energy principle from cognitive neuroscience and the ensuing active inference dynamics with recent advances in variational inference on deep generative models and evolution strategies as efficient large-scale black-box optimisation technique, to introduce the "deep active inference" agent. This agent tries to minimize a variational free energy bound on the average surprise of its sensations, which is motivated by a homeostatic argument. It does so by changing the parameters of its generative model, together with a variational density approximating the posterior distribution over latent variables, given its observations, and by acting on its environment to actively sample input that is likely under its generative model. The internal dynamics of the agent are implemented using deep neural networks, as used in machine learning, and recurrent dynamics, making the deep active inference agent a scalable and very flexible class of active inference agents. Using the mountaincar problem, we show how goal-directed behaviour can be implemented by defining sensible prior expectations on the latent states in the agent's model, that it will try to fulfil. Furthermore, we show that the deep active inference agent can learn a generative model of the environment, which can be sampled from to understand the agent's beliefs about the environment and its interaction with it. 1. Introduction Active Inference (Friston et al., 2006, 2010; Friston, 2012) is a normative theory of brain function derived from the properties required by active agents to survive in dynamic, fluctuating environments. This theory is able to account for many aspects of action and perception as well as anatomic and physiologic features of the brain on one hand (Brown and Friston, 2012; Friston, 2005; Friston et al., 2011; Friston and Kiebel, 2009; Schwartenbeck et al., 2015; Adams et al., 2013), and encompasses many formal theories about brain function on the other (Friston, 2010).