scholarly journals Uncertainty, epistemics and active inference

2017 ◽  
Vol 14 (136) ◽  
pp. 20170376 ◽  
Author(s):  
Thomas Parr ◽  
Karl J. Friston
Keyword(s):  
Inhibition Of Return ◽  
Biological Systems ◽  
Generative Model ◽  
Active Inference ◽  
Sensory Data ◽  
Optimal Decisions ◽  
Link Type ◽  
The Face ◽  
Principled Explanation ◽  
Visual Foraging

Biological systems—like ourselves—are constantly faced with uncertainty. Despite noisy sensory data, and volatile environments, creatures appear to actively maintain their integrity. To account for this remarkable ability to make optimal decisions in the face of a capricious world, we propose a generative model that represents the beliefs an agent might possess about their own uncertainty. By simulating a noisy and volatile environment, we demonstrate how uncertainty influences optimal epistemic (visual) foraging. In our simulations, saccades were deployed less frequently to regions with a lower sensory precision, while a greater volatility led to a shorter inhibition of return. These simulations illustrate a principled explanation for some cardinal aspects of visual foraging—and allow us to propose a correspondence between the representation of uncertainty and ascending neuromodulatory systems, complementing that suggested by Yu & Dayan (Yu & Dayan 2005 Neuron 46 , 681–692. ( doi:10.1016/j.neuron.2005.04.026 )).

scholarly journals Generalised free energy and active inference

2019 ◽  
Vol 113 (5-6) ◽  
pp. 495-513 ◽  
Author(s):  
Thomas Parr ◽  
Karl J. Friston
Keyword(s):  
Free Energy ◽  
Generative Model ◽  
Free Energy Function ◽  
Active Inference ◽  
Future Expectations ◽  
Perceptual Inference ◽  
Energy Functionals ◽  
Sensory Data ◽  
The World ◽  
The Brain

Abstract Active inference is an approach to understanding behaviour that rests upon the idea that the brain uses an internal generative model to predict incoming sensory data. The fit between this model and data may be improved in two ways. The brain could optimise probabilistic beliefs about the variables in the generative model (i.e. perceptual inference). Alternatively, by acting on the world, it could change the sensory data, such that they are more consistent with the model. This implies a common objective function (variational free energy) for action and perception that scores the fit between an internal model and the world. We compare two free energy functionals for active inference in the framework of Markov decision processes. One of these is a functional of beliefs (i.e. probability distributions) about states and policies, but a function of observations, while the second is a functional of beliefs about all three. In the former (expected free energy), prior beliefs about outcomes are not part of the generative model (because they are absorbed into the prior over policies). Conversely, in the second (generalised free energy), priors over outcomes become an explicit component of the generative model. When using the free energy function, which is blind to future observations, we equip the generative model with a prior over policies that ensure preferred (i.e. priors over) outcomes are realised. In other words, if we expect to encounter a particular kind of outcome, this lends plausibility to those policies for which this outcome is a consequence. In addition, this formulation ensures that selected policies minimise uncertainty about future outcomes by minimising the free energy expected in the future. When using the free energy functional—that effectively treats future observations as hidden states—we show that policies are inferred or selected that realise prior preferences by minimising the free energy of future expectations. Interestingly, the form of posterior beliefs about policies (and associated belief updating) turns out to be identical under both formulations, but the quantities used to compute them are not.

scholarly journals Understanding, Explanation, and Active Inference

2021 ◽  
Vol 15 ◽  
Author(s):  
Thomas Parr ◽  
Giovanni Pezzulo
Keyword(s):  
Machine Intelligence ◽  
Generative Model ◽  
Machine Learning Techniques ◽  
Active Inference ◽  
Sensory Data ◽  
Learning Techniques ◽  
The World ◽  
Human Understanding ◽  
Important Challenge ◽  
Level Model

While machine learning techniques have been transformative in solving a range of problems, an important challenge is to understand why they arrive at the decisions they output. Some have argued that this necessitates augmenting machine intelligence with understanding such that, when queried, a machine is able to explain its behaviour (i.e., explainable AI). In this article, we address the issue of machine understanding from the perspective of active inference. This paradigm enables decision making based upon a model of how data are generated. The generative model contains those variables required to explain sensory data, and its inversion may be seen as an attempt to explain the causes of these data. Here we are interested in explanations of one’s own actions. This implies a deep generative model that includes a model of the world, used to infer policies, and a higher-level model that attempts to predict which policies will be selected based upon a space of hypothetical (i.e., counterfactual) explanations—and which can subsequently be used to provide (retrospective) explanations about the policies pursued. We illustrate the construct validity of this notion of understanding in relation to human understanding by highlighting the similarities in computational architecture and the consequences of its dysfunction.

scholarly journals The computational neurology of movement under active inference

Brain ◽  
2021 ◽  
Author(s):  
Thomas Parr ◽  
Jakub Limanowski ◽  
Vishal Rawji ◽  
Karl Friston
Keyword(s):  
Movement Control ◽  
Generative Model ◽  
Clinical Neurology ◽  
Active Inference ◽  
Significant Development ◽  
Prior Probabilities ◽  
Sensory Data ◽  
Limb Coordination ◽  
The Right ◽  
The Brain

Abstract We propose a computational neurology of movement based on the convergence of theoretical neurobiology and clinical neurology. A significant development in the former is the idea that we can frame brain function as a process of (active) inference, in which the nervous system makes predictions about its sensory data. These predictions depend upon an implicit predictive (generative) model used by the brain. This means neural dynamics can be framed as generating actions to ensure sensations are consistent with these predictions—and adjusting predictions when they are not. We illustrate the significance of this formulation for clinical neurology through simulating a clinical examination of the motor system; i.e. an upper limb coordination task. Specifically, we show how tendon reflexes emerge naturally under the right kind of generative model. Through simulated perturbations, pertaining to prior probabilities of this model’s variables, we illustrate the emergence of hyperreflexia and pendular reflexes, reminiscent of neurological lesions in the corticospinal tract and cerebellum. We then turn to the computational lesions causing hypokinesia and deficits of coordination. This in silico lesion-deficit analysis provides an opportunity to revisit classic neurological dichotomies (e.g. pyramidal versus extrapyramidal systems) from the perspective of modern approaches to theoretical neurobiology—and our understanding of the neurocomputational architecture of movement control based on first principles.

scholarly journals THE CONSTRUCTIVE NATURE OF THE STRUCTURAL FEATURES OF THE BUSINESS STRATEGY OF ENTERPRISE DEVELOPMENT IN THE CONTEXT OF GLOBALIZATION

2021 ◽  
pp. 43-45
Author(s):  
А.І. Пляскіна
Keyword(s):  
Complex System ◽  
Economic Activity ◽  
Business Strategy ◽  
Structural Features ◽  
External Factors ◽  
Enterprise Development ◽  
Economic Stability ◽  
Economic Problems ◽  
Optimal Decisions ◽  
The Face

The article presents a comprehensive methodology for the formation of a business strategy for the development of an enterprise, which makes it possible to increase its economic stability in the face of changes in the parameters of the functioning environment. It has been established that ensuring the competitiveness of an enterprise should be based on alternative options for its development, depending on the influence of external factors and the level of risk when focusing on one or another variant of the production and economic activity of the enterprise. Each enterprise is a complex system of interacting elements. Models of strategic management of the enterprise at a choice of strategy are analysed. Building principles and methods of analysis, modelling and management of economic risk is of great importance for making optimal decisions in conditions of uncertainty and conflict in solving certain economic problems, including the formation of business strategy for enterprise development.

scholarly journals PDEparams: Parameter fitting toolbox for partial differential equations in Python

2019 ◽  
Author(s):  
César Parra-Rojas ◽  
Esteban A. Hernandez-Vargas
Keyword(s):  
Parameter Estimation ◽  
Partial Differential Equations ◽  
Differential Equations ◽  
Biological Systems ◽  
Source Code ◽  
Parameter Analysis ◽  
Partial Differential ◽  
Parameter Fitting ◽  
Link Type ◽  
Boot Strapping

AbstractMotivationPartial differential equations (PDEs) is a well-established and powerful tool to simulate multi-cellular biological systems. However, available free tools for validation against data are not established. ThePDEparamsmodule provides flexible functionality in Python for parameter estimation in PDE models.ResultsThePDEparamsmodule provides a flexible interface and readily accommodates different parameter analysis tools in PDE models such as computation of likelihood profiles, and parametric boot-strapping, along with direct visualisation of the results. To our knowledge, it is the first open, freely available tool for parameter fitting of PDE models.Availability and implementationThePDEparamsmodule is distributed under the MIT license. The source code, usage instructions and step-by-step examples are freely available on GitHub atgithub.com/systemsmedicine/[email protected]

scholarly journals PID Control as a Process of Active Inference with Linear Generative Models

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 257 ◽  
Author(s):  
Manuel Baltieri ◽  
Christopher Buckley
Keyword(s):  
Free Energy ◽  
Biological Systems ◽  
Generative Models ◽  
Pid Controllers ◽  
Mathematical Framework ◽  
Prediction Errors ◽  
Active Inference ◽  
General Process ◽  
Brain Functions ◽  
Theoretical Approaches

In the past few decades, probabilistic interpretations of brain functions have become widespread in cognitive science and neuroscience. In particular, the free energy principle and active inference are increasingly popular theories of cognitive functions that claim to offer a unified understanding of life and cognition within a general mathematical framework derived from information and control theory, and statistical mechanics. However, we argue that if the active inference proposal is to be taken as a general process theory for biological systems, it is necessary to understand how it relates to existing control theoretical approaches routinely used to study and explain biological systems. For example, recently, PID (Proportional-Integral-Derivative) control has been shown to be implemented in simple molecular systems and is becoming a popular mechanistic explanation of behaviours such as chemotaxis in bacteria and amoebae, and robust adaptation in biochemical networks. In this work, we will show how PID controllers can fit a more general theory of life and cognition under the principle of (variational) free energy minimisation when using approximate linear generative models of the world. This more general interpretation also provides a new perspective on traditional problems of PID controllers such as parameter tuning as well as the need to balance performances and robustness conditions of a controller. Specifically, we then show how these problems can be understood in terms of the optimisation of the precisions (inverse variances) modulating different prediction errors in the free energy functional.

Replicating Neuroscience Observations on ML/MF and AM Face Patches by Deep Generative Model

2019 ◽  
Vol 31 (12) ◽  
pp. 2348-2367
Author(s):  
Tian Han ◽  
Xianglei Xing ◽  
Jiawen Wu ◽  
Ying Nian Wu
Keyword(s):  
Linear Relationship ◽  
Latent Variables ◽  
Generative Model ◽  
Appearance Model ◽  
Shape Model ◽  
Face Images ◽  
Variational Autoencoder ◽  
The Face ◽  
Face Stimuli ◽  
Strong Linear Relationship

A recent Cell paper (Chang & Tsao, 2017 ) reports an interesting discovery. For the face stimuli generated by a pretrained active appearance model (AAM), the responses of neurons in the areas of the primate brain that are responsible for face recognition exhibit a strong linear relationship with the shape variables and appearance variables of the AAM that generates the face stimuli. In this letter, we show that this behavior can be replicated by a deep generative model, the generator network, that assumes that the observed signals are generated by latent random variables via a top-down convolutional neural network. Specifically, we learn the generator network from the face images generated by a pretrained AAM model using a variational autoencoder, and we show that the inferred latent variables of the learned generator network have a strong linear relationship with the shape and appearance variables of the AAM model that generates the face images. Unlike the AAM model, which has an explicit shape model where the shape variables generate the control points or landmarks, the generator network has no such shape model and shape variables. Yet it can learn the shape knowledge in the sense that some of the latent variables of the learned generator network capture the shape variations in the face images generated by AAM.

scholarly journals Deeply Felt Affect: The Emergence of Valence in Deep Active Inference

2020 ◽  
pp. 1-49
Author(s):  
Casper Hesp ◽  
Ryan Smith ◽  
Thomas Parr ◽  
Micah Allen ◽  
Karl J. Friston ◽  
...  
Keyword(s):  
Affective State ◽  
Emotional Valence ◽  
Face Validity ◽  
Active Inference ◽  
Mental Action ◽  
Sensory Data ◽  
Context Learning ◽  
Domain Generality ◽  
Internal Action ◽  
Action Model

The positive-negative axis of emotional valence has long been recognized as fundamental to adaptive behavior, but its origin and underlying function have largely eluded formal theorizing and computational modeling. Using deep active inference, a hierarchical inference scheme that rests on inverting a model of how sensory data are generated, we develop a principled Bayesian model of emotional valence. This formulation asserts that agents infer their valence state based on the expected precision of their action model—an internal estimate of overall model fitness (“subjective fitness”). This index of subjective fitness can be estimated within any environment and exploits the domain generality of second-order beliefs (beliefs about beliefs). We show how maintaining internal valence representations allows the ensuing affective agent to optimize confidence in action selection preemptively. Valence representations can in turn be optimized by leveraging the (Bayes-optimal) updating term for subjective fitness, which we label affective charge (AC). AC tracks changes in fitness estimates and lends a sign to otherwise unsigned divergences between predictions and outcomes. We simulate the resulting affective inference by subjecting an in silico affective agent to a T-maze paradigm requiring context learning, followed by context reversal. This formulation of affective inference offers a principled account of the link between affect, (mental) action, and implicit metacognition. It characterizes how a deep biological system can infer its affective state and reduce uncertainty about such inferences through internal action (i.e., top-down modulation of priors that underwrite confidence). Thus, we demonstrate the potential of active inference to provide a formal and computationally tractable account of affect. Our demonstration of the face validity and potential utility of this formulation represents the first step within a larger research program. Next, this model can be leveraged to test the hypothesized role of valence by fitting the model to behavioral and neuronal responses.

scholarly journals Building a Better Test for Piperacillin-Tazobactam Susceptibility Testing: Would that It Were So Simple (It's Complicated)

2019 ◽  
Vol 58 (2) ◽  
Author(s):  
Andrew Henderson ◽  
Romney Humphries
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Clinical Microbiology ◽  
Susceptibility Testing ◽  
Resistance Mechanisms ◽  
Performance Data ◽  
Analytical Performance ◽  
Content Type ◽  
Link Type ◽  
The Face

ABSTRACT In this issue of the Journal of Clinical Microbiology, S. García-Fernandez, Y. Bala, T. Armstrong, M. Garcia-Castillo, et al. (J Clin Microbiol 58:e01042-19, 2020, https://doi.org/10.1128/JCM.01042-19) describe the performance of a reformulated Etest for piperacillin-tazobactam. The analytical performance data are excellent, but in the face of recent emerging data on the inefficacy of piperacillin-tazobactam for certain organisms that test susceptible, the value of piperacillin-tazobactam MICs is controversial. Evaluation of MICs in the context of the modal MIC for Enterobacterales and Pseudomonas aeruginosa, the variability of MIC tests, and, possibly, resistance mechanisms is important to the optimum use of this antimicrobial.

scholarly journals Inhibition of return in a visual foraging task in non-human subjects

2012 ◽  
Vol 74 ◽  
pp. 2-9 ◽  
Author(s):  
Solmaz Shariat Torbaghan ◽  
Daniel Yazdi ◽  
Koorosh Mirpour ◽  
James W. Bisley
Keyword(s):  
Inhibition Of Return ◽  
Human Subjects ◽  
Visual Foraging ◽  
Foraging Task
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