Hierarchical predictive coding in distributed pain circuits DOI Creative Commons
Zhe Chen

Frontiers in Neural Circuits, Journal Year: 2023, Volume and Issue: 17

Published: March 3, 2023

Predictive coding is a computational theory on describing how the brain perceives and acts, which has been widely adopted in sensory processing motor control. Nociceptive pain involves large distributed network of circuits. However, it still unknown whether this completely decentralized or requires networkwide coordination. Multiple lines evidence from human animal studies have suggested that cingulate cortex insula (cingulate-insula network) are two major hubs mediating information afferents spinothalamic inputs, whereas subregions cortices distinct projections functional roles. In mini-review, we propose an updated hierarchical predictive framework for perception discuss its related computational, algorithmic, implementation issues. We suggest active inference as generalized algorithm, hierarchically organized traveling waves independent neural oscillations plausible mechanism to integrate bottom-up top-down across

Language: Английский

The free energy principle made simpler but not too simple DOI Creative Commons
Karl Friston, Lancelot Da Costa, Noor Sajid

et al.

Physics Reports, Journal Year: 2023, Volume and Issue: 1024, P. 1 - 29

Published: June 1, 2023

This paper provides a concise description of the free energy principle, starting from formulation random dynamical systems in terms Langevin equation and ending with Bayesian mechanics that can be read as physics sentience. It rehearses key steps using standard results statistical physics. These entail (i) establishing particular partition states based upon conditional independencies inherit sparsely coupled dynamics, (ii) unpacking implications this inference (iii) describing paths variational principle least action. Teleologically, offers normative account self-organisation optimal design decision-making, sense maximising marginal likelihood or model evidence. In summary, world systems, we end up sentient behaviour interpreted self-evidencing; namely, self-assembly, autopoiesis active inference.

Language: Английский

Citations

86
Towards a computational phenomenology of mental action: modelling meta-awareness and attentional control with deep parametric active inference DOI Creative Commons
Lars Sandved-Smith, Casper Hesp, Jérémie Mattout

et al.

Neuroscience of Consciousness, Journal Year: 2021, Volume and Issue: 2021(1)

Published: Aug. 26, 2021

Abstract Meta-awareness refers to the capacity explicitly notice current content of consciousness and has been identified as a key component for successful control cognitive states, such deliberate direction attention. This paper proposes formal model meta-awareness attentional using hierarchical active inference. To do so, we cast mental action policy selection over higher-level states add further level that modulate expected confidence (precision) in mapping between observations hidden states. We simulate example mind-wandering its regulation during task involving sustained selective attention on perceptual object. provides computational case study an inferential architecture is apt enable emergence these central components human phenomenology, namely, ability access propose this approach can be generalized other hence, first steps towards development phenomenology more broadly our monitor own Future work will focus fitting with qualitative, behavioural, neural data.

Language: Английский

Citations

91
From Generative Models to Generative Passages: A Computational Approach to (Neuro) Phenomenology DOI Creative Commons
Maxwell J. D. Ramstead, Anil K. Seth, Casper Hesp

et al.

Review of Philosophy and Psychology, Journal Year: 2022, Volume and Issue: 13(4), P. 829 - 857

Published: March 18, 2022

This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. Our approach can be described as

Language: Английский

Citations

51
pymdp: A Python library for active inference in discrete state spaces DOI Creative Commons
Conor Heins, Beren Millidge, Daphne Demekas

et al.

The Journal of Open Source Software, Journal Year: 2022, Volume and Issue: 7(73), P. 4098 - 4098

Published: May 4, 2022

Active inference is an account of cognition and behavior in complex systems which brings together action, perception, learning under the theoretical mantle Bayesian inference. has seen growing applications academic research, especially fields that seek to model human or animal behavior. While recent years, some code arising from active literature been written open source languages like Python Julia, to-date, most popular software for simulating agents DEM toolbox SPM, a MATLAB library originally developed statistical analysis modelling neuroimaging data. Increasing interest inference, manifested both terms sheer number as well diversifying across scientific disciplines, thus created need generic, widely-available, user-friendly open-source computing Python. The package we present here, pymdp (see https://github.com/infer-actively/pymdp), represents significant step this direction: namely, provide first with partially-observable Markov Decision Processes POMDPs. We review package's structure explain its advantages modular design customizability, while providing in-text blocks along way demonstrate how it can be used build run processes ease. increase accessibility exposure framework researchers, engineers, developers diverse disciplinary backgrounds. In spirit software, also hope spurs new innovation, development, collaboration community.

Language: Английский

Citations

44
Interpersonal attunement in social interactions: from collective psychophysiology to inter- personalized psychiatry and beyond DOI Creative Commons
Dimitris Bolis, Guillaume Dumas, Leonhard Schilbach

et al.

Philosophical Transactions of the Royal Society B Biological Sciences, Journal Year: 2022, Volume and Issue: 378(1870)

Published: Dec. 26, 2022

In this article, we analyse social interactions, drawing on diverse points of views, ranging from dialectics, second-person neuroscience and enactivism to dynamical systems, active inference machine learning. To end, define interpersonal attunement as a set multi-scale processes building up materializing expectations-put simply, anticipating interacting with others ourselves. While cultivating negotiating common ground, via communication culture-building activities, are indispensable for the survival individual, relevant mechanisms have been largely considered in isolation. Here, collective psychophysiology, argue, can lend itself fine-tuned analysis without neglecting individual. On other hand, an mismatch expectations lead breakdown isolation known negatively affect mental health. regard, review psychopathology terms misattunement, conceptualizing psychiatric disorders interaction, describe how individual health is inextricably linked interaction. By doing so, foresee avenues inter-personalized psychiatry, which moves static spectrum dynamic relational space, focusing multi-faceted interaction help promote This article part theme issue 'Concepts interaction: engagement inner experiences'.

Language: Английский

Citations

43
Cognitive effort and active inference DOI Creative Commons
Thomas Parr, Emma Holmes, Karl Friston

et al.

Neuropsychologia, Journal Year: 2023, Volume and Issue: 184, P. 108562 - 108562

Published: April 18, 2023

This paper aims to integrate some key constructs in the cognitive neuroscience of control and executive function by formalising notion (or mental) effort terms active inference. To do so, we call upon a task used neuropsychology assess impulse inhibition-a Stroop task. In this task, participants must suppress read colour word instead report text word. The is characteristically effortful, unpack theory mental which, perform accurately, overcome prior beliefs about how they would normally act. However, our interest here not overt action, but covert (mental) action. Mental actions change have no (direct) effect on outside world-much like deploying attention. account as action lets us generate multimodal (choice, reaction time, electrophysiological) data sort might expect from human participant engaging We analyse parameters determining influence simulated responses demonstrate that-when provided only with performance data-these can be recovered, are within certain range.

Language: Английский

Citations

32
The empirical status of predictive coding and active inference DOI
Rowan Hodson, Marishka Mehta, Ryan Smith

et al.

Neuroscience & Biobehavioral Reviews, Journal Year: 2023, Volume and Issue: 157, P. 105473 - 105473

Published: Nov. 28, 2023

Language: Английский

Citations

29
Fundamental Research Challenges for Distributed Computing Continuum Systems DOI Creative Commons
Víctor Casamayor Pujol, Andrea Morichetta, Ilir Murturi

et al.

Information, Journal Year: 2023, Volume and Issue: 14(3), P. 198 - 198

Published: March 22, 2023

This article discusses four fundamental topics for future Distributed Computing Continuum Systems: their representation, model, lifelong learning, and business model. Further, it presents techniques concepts that can be useful to define these specifically Systems. Finally, this a broad view of the synergies among presented technique enable development

Language: Английский

Citations

26
An active inference model of hierarchical action understanding, learning and imitation DOI Creative Commons
Riccardo Proietti, Giovanni Pezzulo, Alessia Tessari

et al.

Physics of Life Reviews, Journal Year: 2023, Volume and Issue: 46, P. 92 - 118

Published: June 5, 2023

We advance a novel active inference model of the cognitive processing that underlies acquisition hierarchical action repertoire and its use for observation, understanding imitation. illustrate in four simulations tennis learner who observes teacher performing shots, forms representations observed actions, imitates them. Our show agent's oculomotor activity implements an information sampling strategy permits inferring kinematic aspects movement, which lie at lowest level hierarchy. In turn, this low-level supports higher-level inferences about deeper actions: proximal goals intentions. Finally, inferred can steer imitative responses, but interfere with execution different actions. provides unified account understanding, learning imitation helps explain neurobiological underpinnings visuomotor cognition, including multiple routes dorsal ventral streams mirror mechanisms.

Language: Английский

Citations

24
Introducing ActiveInference.jl: A Julia Library for Simulation and Parameter Estimation with Active Inference Models DOI Creative Commons

Samuel William Nehrer,

Jonathan Ehrenreich Laursen, Conor Heins

et al.

Entropy, Journal Year: 2025, Volume and Issue: 27(1), P. 62 - 62

Published: Jan. 12, 2025

We introduce a new software package for the Julia programming language, library ActiveInference.jl. To make active inference agents with Partially Observable Markov Decision Process (POMDP) generative models available to growing research community using Julia, we re-implemented pymdp Python. ActiveInference.jl is compatible cutting-edge libraries designed cognitive and behavioural modelling, as it used in computational psychiatry, science neuroscience. This means that POMDP can now be easily fit empirically observed behaviour sampling, well variational methods. In this article, show how makes building straightforward, enables researchers use them simulation, fitting data or performing model comparison.

Language: Английский

Citations

1