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 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 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 Generative models, language and active inference

2020 ◽  
Author(s):  
Karl Friston ◽  
Thomas Parr ◽  
Yan Yufik ◽  
Noor Sajid ◽  
Cathy J. Price ◽  
...  
Keyword(s):  
Language Processing ◽  
Hierarchical Models ◽  
Message Passing ◽  
Generative Models ◽  
Generative Model ◽  
Active Inference ◽  
Factorial Structure ◽  
The Rich ◽  
The Right

This paper presents a biologically plausible generative model and inference scheme that is capable of simulating the generation and comprehension of language, when synthetic subjects talk to each other. Building on active inference formulations of dyadic interactions, we simulate linguistic exchange to explore generative models that support dialogues. These models employ high-order interactions among abstract (discrete) states in deep (hierarchical) models. The sequential nature of language processing mandates generative models with a particular factorial structure—necessary to accommodate the rich combinatorics of language. We illustrate this by simulating a synthetic subject who can play the ‘Twenty Questions’ game. In this game, synthetic subjects take the role of the questioner or answerer, using the same generative model. This simulation setup is used to illustrate some key architectural points and demonstrate that many behavioural and neurophysiological correlates of language processing emerge under variational (marginal) message passing, given the right kind of generative model. For example, we show that theta-gamma coupling is an emergent property of belief updating, when listening to another.

scholarly journals Generative Models for Active Vision

2021 ◽  
Vol 15 ◽  
Author(s):  
Thomas Parr ◽  
Noor Sajid ◽  
Lancelot Da Costa ◽  
M. Berk Mirza ◽  
Karl J. Friston
Keyword(s):  
Information Gain ◽  
Cell Activity ◽  
Active Vision ◽  
Oculomotor System ◽  
Generative Models ◽  
Generative Model ◽  
Retinal Cell ◽  
Proprioceptive Information ◽  
Active Inference ◽  
The Brain

The active visual system comprises the visual cortices, cerebral attention networks, and oculomotor system. While fascinating in its own right, it is also an important model for sensorimotor networks in general. A prominent approach to studying this system is active inference—which assumes the brain makes use of an internal (generative) model to predict proprioceptive and visual input. This approach treats action as ensuring sensations conform to predictions (i.e., by moving the eyes) and posits that visual percepts are the consequence of updating predictions to conform to sensations. Under active inference, the challenge is to identify the form of the generative model that makes these predictions—and thus directs behavior. In this paper, we provide an overview of the generative models that the brain must employ to engage in active vision. This means specifying the processes that explain retinal cell activity and proprioceptive information from oculomotor muscle fibers. In addition to the mechanics of the eyes and retina, these processes include our choices about where to move our eyes. These decisions rest upon beliefs about salient locations, or the potential for information gain and belief-updating. A key theme of this paper is the relationship between “looking” and “seeing” under the brain's implicit generative model of the visual world.

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.

The brain structure and neurosecretory cells of noctuid moth Anadevidia peponis: Noctuidae

1990 ◽  
Vol 48 (3) ◽  
pp. 436-437
Author(s):  
M. Sato ◽  
Y. Ogawa ◽  
M. Sasaki ◽  
T. Matsuo
Keyword(s):  
Biological Clock ◽  
Virgin Female ◽  
Basic Structure ◽  
Fixed Time ◽  
Neurosecretory Cells ◽  
Nerve Cells ◽  
Noctuid Moth ◽  
The Right ◽  
20 Nm ◽  
The Brain

A virgin female of the noctuid moth, a kind of noctuidae that eats cucumis, etc. performs calling at a fixed time of each day, depending on the length of a day. The photoreceptors that induce this calling are located around the neurosecretory cells (NSC) in the central portion of the protocerebrum. Besides, it is considered that the female’s biological clock is located also in the cerebral lobe. In order to elucidate the calling and the function of the biological clock, it is necessary to clarify the basic structure of the brain. The observation results of 12 or 30 day-old noctuid moths showed that their brains are basically composed of an outer and an inner portion-neural lamella (about 2.5 μm) of collagen fibril and perineurium cells. Furthermore, nerve cells surround the cerebral lobes, in which NSCs, mushroom bodies, and central nerve cells, etc. are observed. The NSCs are large-sized (20 to 30 μm dia.) cells, which are located in the pons intercerebralis of the head section and at the rear of the mushroom body (two each on the right and left). Furthermore, the cells were classified into two types: one having many free ribosoms 15 to 20 nm in dia. and the other having granules 150 to 350 nm in dia. (Fig. 1).

Some Comments on the Highest Attainable Standard of Health

2020 ◽  
Vol 26 (2) ◽  
pp. 134-140
Author(s):  
Gabriela Belova ◽  
Stanislav Pavlov
Keyword(s):  
Health Resources ◽  
Right To Health ◽  
International Standards ◽  
Special Rapporteur ◽  
Health It ◽  
Significant Development ◽  
Cultural Rights ◽  
Optional Protocol ◽  
The Social ◽  
The Right

AbstractThe last decades present a significant development of the economic, social and cultural rights and specifically, the right to health. Until 2000, the right to health has not been interpreted officially. By providing international standards, General Comment No.14 on the right to the Highest Attainable Standard of Health has led to wider agreement that the right to health includes the social determinants of health such as access to various conditions, services, goods or facilities that are crucial for its implementation. The Reports of the Special Rapporteur on the right to health within the UN human rights system have contributed to the process of gaining the greater clarity about the right to health. It is obvious that achieving the highest attainable level of health depends on the principle of progressive implementation and the availability of the necessary health resources. The possibility individual complaints to be considered by the Committee on Economic Social and Cultural Rights was introduced with the Optional Protocol to the International Covenant on Economic, Social and Cultural Rights, entered into force in 2013.

ASSESSMENT OF PHYSIOLOGICAL PARAMETERS OF THE TRIGEMINAL AND COCHLEOVESTIBULAR SYSTEMS IN TEMPOROMANDIBULAR JOINT PAIN DYSFUNCTION

2020 ◽  
Vol 17 (2) ◽  
pp. 110-120
Author(s):  
N.D. Sorokina ◽  
◽  
L.R. Shahalieva ◽  
S.S. Pertsov ◽  
L.V. Polma ◽  
...  
Keyword(s):  
Nervous System ◽  
Evoked Potentials ◽  
Temporomandibular Joint ◽  
Differential Diagnostics ◽  
Autonomous Nervous System ◽  
Dental Diseases ◽  
Grade Ii ◽  
The Right ◽  
Distal Occlusion ◽  
The Brain

One of the most common causes of chronic pain in the facial region, including in the trigeminal nerve link, which is not associated with dental diseases, is pain dysfunction of the temporomandibular joint. At the same time, there is evidence in the literature that there are relationships between pain dysfunction of the temporomandibular joint, abnormal occlusion, cervical-muscular tonic phenomena, postural disorders, dysfunction of the Autonomous nervous system and cochleovestibular manifestations. At the same time, neurophysiological indicators of functional disorders in the maxillofacial region and intersystem interactions in pain dysfunction of the temporomandibular joint are insufficiently studied.Goal. The aim of the work is to evaluate the neurophysiological features of trigeminal afferentation in terms of trigeminal somatosensory evoked potentials (TSWP) and the auditory conducting system of the brain in terms of acoustic stem evoked potentials (ASVP) in distal occlusion of the dentition with pain dysfunction of the temporomandibular joint (TMJ) in comparison with physiological occlusion in students 18-21 years old. Material and methods. The main study included 41 students with distal occlusion (21 girls and 20 boys), (grade II Engl, symmetrically right and left in 14 people, and grade II Engl on the left and grade I on the right in 12 people, grade I on the left and grade II on the right in 15 people). All respondents with distal occlusion and who were practically healthy signed an informed consent to participate in the study. We used complex orthodontic methods of examination, subjective degree of severity and intensity of pain in the TMJ, assessment of the Autonomous nervous system (samples and tests), and neurophysiological methods for assessing TSVP and ASVP. Results. Significant differences in ASEP parameters were found in the group of respondents with distal occlusion in the form of a decrease in the latency period of peak I, III, and V compared to physiological occlusion, that correlated with the subjective assessment (in points) of cochleovestibular disorders. According to the TSVP study, a decrease in the duration of latent periods was found, which indicates an increased excitability of non-specific brain stem structures at the medullo-ponto-mesencephalic level compared to the control group. Conclusions. The results obtained are supposed to be used for differential diagnostics, including such dental diseases as TMJ pain dysfunction, occlusion abnormalities accompanied by pain syndrome. Additional functional diagnostics of multi-modal VP of the brain (acoustic evoked potentials, trigeminal evoked potentials) can be performed in conjunction with indicators of autonomic nervous system dysfunction, with parameters of severity of clinical symptoms of cochleovestibular disorders, musculoskeletal dysfunction the maxillofacial area, with indicators of pain, which will determine the tactics and effectiveness of subsequent treatment.

Consciousness

2018 ◽  
Author(s):  
Anil K. Seth
Keyword(s):  
Conscious Experience ◽  
Real Problem ◽  
Active Inference ◽  
Biological Mechanisms ◽  
Specific Content ◽  
Brain Responses ◽  
Biomimetic Approach ◽  
Multiple Levels ◽  
In Virtue Of ◽  
The Brain

Consciousness is perhaps the most familiar aspect of our existence, yet we still do not know its biological basis. This chapter outlines a biomimetic approach to consciousness science, identifying three principles linking properties of conscious experience to potential biological mechanisms. First, conscious experiences generate large quantities of information in virtue of being simultaneously integrated and differentiated. Second, the brain continuously generates predictions about the world and self, which account for the specific content of conscious scenes. Third, the conscious self depends on active inference of self-related signals at multiple levels. Research following these principles helps move from establishing correlations between brain responses and consciousness towards explanations which account for phenomenological properties—addressing what can be called the “real problem” of consciousness. The picture that emerges is one in which consciousness, mind, and life, are tightly bound together—with implications for any possible future “conscious machines.”

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