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Entropy
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From Functional Imaging to Free Energy—Dedicated to Professor Karl Friston...
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From Functional Imaging to Free Energy—Dedicated to Professor Karl Friston on the Occasion of His 65th Birthday

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Entropy and Biology".

Deadline for manuscript submissions: 16 May 2024 | Viewed by 10725

Special Issue Editors

Dr. Thomas Parr

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Guest Editor
Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
Interests: active inference; Bayesian mechanics; theoretical neurobiology; computational neurology
Special Issues, Collections and Topics in MDPI journals
Dr. Giovanni Pezzulo

E-Mail Website
Guest Editor
Institute of Cognitive Sciences and Technologies, National Research Council, 00185 Rome, Italy
Interests: cognitive science; cognitive robotics; probabilistic models of brain and cognition
Prof. Dr. Rosalyn Moran

E-Mail Website
Guest Editor
1. Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
2. Stanhope AI, London SW10 0JG, UK
Interests: computational neuroscience; Bayesian inference (variational principles)
Dr. Maxwell Ramstead

E-Mail Website
Guest Editor
1. VERSES AI Research Lab, Los Angeles, CA 90016, USA
2. Wellcome Centre for Human Neuroimaging, University College London, London WC1E 6BT, UK
Interests: active inference; philosophy of psychiatry; cognitive science; ecological cognition; hermeneutics
Special Issues, Collections and Topics in MDPI journals
Dr. Axel Constant

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Guest Editor
School of Engineering and Informatics, The University of Sussex, Brighton BN1 9RH, UK
Interests: computational, evolutionary, and cultural psychiatry; evolutionary computation; human social cognition
Dr. Anjali Bhat

E-Mail Website
Guest Editor
Wellcome Centre for Human Neuroimaging, University College London, London WC1E 6BT, UK
Interests: neuroscience

Special Issue Information

Dear Colleagues,

Karl Friston’s contributions to the brain sciences are difficult to overstate. From the development of statistical parametric mapping—a ubiquitous technique employed in the analysis of functional neuroimaging data—to the free energy principle and active inference, his ideas have changed the way in which many of us engage with neuroscience, psychology, biology, and the philosophy of the mind. As if his unparalleled influence in understanding how the brain works were not enough, Friston’s work dissolved interdisciplinary boundaries (or perhaps blankets) and has informed fields as diverse as statistics, epidemiology, morphogenesis, climate science, the physics of sentience, evolution, and artificial intelligence.

This Special Issue—on the occasion of Karl Friston's 65th birthday—aims to celebrate his body of work and the many directions of research—and the many researchers—young or more experienced—who it continues to inspire. We welcome submissions that illustrate how collective generative models have been optimised through the application of dynamic causal modelling (to neural circuits, pandemics, or climates) through the study of predictive coding and active inference in the brain, as well as through structure learning by curious machines.

Dr. Thomas Parr
Dr. Giovanni Pezzulo
Prof. Dr. Rosalyn Moran
Dr. Maxwell Ramstead
Dr. Axel Constant
Dr. Anjali Bhat 
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • active inference
  • free-energy principle
  • Bayesian
  • variational inference
  • functional imaging
  • predictive coding

Published Papers (6 papers)

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17 pages, 318 KiB  
Article
Shared Protentions in Multi-Agent Active Inference
by Mahault Albarracin, Riddhi J. Pitliya, Toby St. Clere Smithe, Daniel Ari Friedman, Karl Friston and Maxwell J. D. Ramstead
Entropy 2024, 26(4), 303; https://0-doi-org.brum.beds.ac.uk/10.3390/e26040303 - 29 Mar 2024
Viewed by 2890
Abstract
In this paper, we unite concepts from Husserlian phenomenology, the active inference framework in theoretical biology, and category theory in mathematics to develop a comprehensive framework for understanding social action premised on shared goals. We begin with an overview of Husserlian phenomenology, focusing [...] Read more.
In this paper, we unite concepts from Husserlian phenomenology, the active inference framework in theoretical biology, and category theory in mathematics to develop a comprehensive framework for understanding social action premised on shared goals. We begin with an overview of Husserlian phenomenology, focusing on aspects of inner time-consciousness, namely, retention, primal impression, and protention. We then review active inference as a formal approach to modeling agent behavior based on variational (approximate Bayesian) inference. Expanding upon Husserl’s model of time consciousness, we consider collective goal-directed behavior, emphasizing shared protentions among agents and their connection to the shared generative models of active inference. This integrated framework aims to formalize shared goals in terms of shared protentions, and thereby shed light on the emergence of group intentionality. Building on this foundation, we incorporate mathematical tools from category theory, in particular, sheaf and topos theory, to furnish a mathematical image of individual and group interactions within a stochastic environment. Specifically, we employ morphisms between polynomial representations of individual agent models, allowing predictions not only of their own behaviors but also those of other agents and environmental responses. Sheaf and topos theory facilitates the construction of coherent agent worldviews and provides a way of representing consensus or shared understanding. We explore the emergence of shared protentions, bridging the phenomenology of temporal structure, multi-agent active inference systems, and category theory. Shared protentions are highlighted as pivotal for coordination and achieving common objectives. We conclude by acknowledging the intricacies stemming from stochastic systems and uncertainties in realizing shared goals. Full article
(This article belongs to the Special Issue From Functional Imaging to Free Energy—Dedicated to Professor Karl Friston on the Occasion of His 65th Birthday)
17 pages, 2823 KiB  
Article
Markov Blankets and Mirror Symmetries—Free Energy Minimization and Mesocortical Anatomy
by James Wright and Paul Bourke
Entropy 2024, 26(4), 287; https://0-doi-org.brum.beds.ac.uk/10.3390/e26040287 - 27 Mar 2024
Viewed by 1040
Abstract
A theoretical account of development in mesocortical anatomy is derived from the free energy principle, operating in a neural field with both Hebbian and anti-Hebbian neural plasticity. An elementary structural unit is proposed, in which synaptic connections at mesoscale are arranged in paired [...] Read more.
A theoretical account of development in mesocortical anatomy is derived from the free energy principle, operating in a neural field with both Hebbian and anti-Hebbian neural plasticity. An elementary structural unit is proposed, in which synaptic connections at mesoscale are arranged in paired patterns with mirror symmetry. Exchanges of synaptic flux in each pattern form coupled spatial eigenmodes, and the line of mirror reflection between the paired patterns operates as a Markov blanket, so that prediction errors in exchanges between the pairs are minimized. The theoretical analysis is then compared to the outcomes from a biological model of neocortical development, in which neuron precursors are selected by apoptosis for cell body and synaptic connections maximizing synchrony and also minimizing axonal length. It is shown that this model results in patterns of connection with the anticipated mirror symmetries, at micro-, meso- and inter-arial scales, among lateral connections, and in cortical depth. This explains the spatial organization and functional significance of neuron response preferences, and is compatible with the structural form of both columnar and noncolumnar cortex. Multi-way interactions of mirrored representations can provide a preliminary anatomically realistic model of cortical information processing. Full article
(This article belongs to the Special Issue From Functional Imaging to Free Energy—Dedicated to Professor Karl Friston on the Occasion of His 65th Birthday)
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16 pages, 609 KiB  
Article
Principled Limitations on Self-Representation for Generic Physical Systems
by Chris Fields, James F. Glazebrook and Michael Levin
Entropy 2024, 26(3), 194; https://0-doi-org.brum.beds.ac.uk/10.3390/e26030194 - 24 Feb 2024
Viewed by 3217
Abstract
The ideas of self-observation and self-representation, and the concomitant idea of self-control, pervade both the cognitive and life sciences, arising in domains as diverse as immunology and robotics. Here, we ask in a very general way whether, and to what extent, these ideas [...] Read more.
The ideas of self-observation and self-representation, and the concomitant idea of self-control, pervade both the cognitive and life sciences, arising in domains as diverse as immunology and robotics. Here, we ask in a very general way whether, and to what extent, these ideas make sense. Using a generic model of physical interactions, we prove a theorem and several corollaries that severely restrict applicable notions of self-observation, self-representation, and self-control. We show, in particular, that adding observational, representational, or control capabilities to a meta-level component of a system cannot, even in principle, lead to a complete meta-level representation of the system as a whole. We conclude that self-representation can at best be heuristic, and that self models cannot, in general, be empirically tested by the systems that implement them. Full article
(This article belongs to the Special Issue From Functional Imaging to Free Energy—Dedicated to Professor Karl Friston on the Occasion of His 65th Birthday)
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32 pages, 5447 KiB  
Article
Spatial and Temporal Hierarchy for Autonomous Navigation Using Active Inference in Minigrid Environment
by Daria de Tinguy, Toon Van de Maele, Tim Verbelen and Bart Dhoedt
Entropy 2024, 26(1), 83; https://0-doi-org.brum.beds.ac.uk/10.3390/e26010083 - 18 Jan 2024
Viewed by 1076
Abstract
Robust evidence suggests that humans explore their environment using a combination of topological landmarks and coarse-grained path integration. This approach relies on identifiable environmental features (topological landmarks) in tandem with estimations of distance and direction (coarse-grained path integration) to construct cognitive maps of [...] Read more.
Robust evidence suggests that humans explore their environment using a combination of topological landmarks and coarse-grained path integration. This approach relies on identifiable environmental features (topological landmarks) in tandem with estimations of distance and direction (coarse-grained path integration) to construct cognitive maps of the surroundings. This cognitive map is believed to exhibit a hierarchical structure, allowing efficient planning when solving complex navigation tasks. Inspired by human behaviour, this paper presents a scalable hierarchical active inference model for autonomous navigation, exploration, and goal-oriented behaviour. The model uses visual observation and motion perception to combine curiosity-driven exploration with goal-oriented behaviour. Motion is planned using different levels of reasoning, i.e., from context to place to motion. This allows for efficient navigation in new spaces and rapid progress toward a target. By incorporating these human navigational strategies and their hierarchical representation of the environment, this model proposes a new solution for autonomous navigation and exploration. The approach is validated through simulations in a mini-grid environment. Full article
(This article belongs to the Special Issue From Functional Imaging to Free Energy—Dedicated to Professor Karl Friston on the Occasion of His 65th Birthday)
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34 pages, 23651 KiB  
Article
Incremental Learning of Goal-Directed Actions in a Dynamic Environment by a Robot Using Active Inference
by Takazumi Matsumoto, Wataru Ohata and Jun Tani
Entropy 2023, 25(11), 1506; https://0-doi-org.brum.beds.ac.uk/10.3390/e25111506 - 31 Oct 2023
Viewed by 894
Abstract
This study investigated how a physical robot can adapt goal-directed actions in dynamically changing environments, in real-time, using an active inference-based approach with incremental learning from human tutoring examples. Using our active inference-based model, while good generalization can be achieved with appropriate parameters, [...] Read more.
This study investigated how a physical robot can adapt goal-directed actions in dynamically changing environments, in real-time, using an active inference-based approach with incremental learning from human tutoring examples. Using our active inference-based model, while good generalization can be achieved with appropriate parameters, when faced with sudden, large changes in the environment, a human may have to intervene to correct actions of the robot in order to reach the goal, as a caregiver might guide the hands of a child performing an unfamiliar task. In order for the robot to learn from the human tutor, we propose a new scheme to accomplish incremental learning from these proprioceptive–exteroceptive experiences combined with mental rehearsal of past experiences. Our experimental results demonstrate that using only a few tutoring examples, the robot using our model was able to significantly improve its performance on new tasks without catastrophic forgetting of previously learned tasks. Full article
(This article belongs to the Special Issue From Functional Imaging to Free Energy—Dedicated to Professor Karl Friston on the Occasion of His 65th Birthday)
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9 pages, 196 KiB  
Opinion
Friston, Free Energy, and Psychoanalytic Psychotherapy
by Jeremy Holmes
Entropy 2024, 26(4), 343; https://0-doi-org.brum.beds.ac.uk/10.3390/e26040343 - 18 Apr 2024
Viewed by 394
Abstract
This paper outlines the ways in which Karl Friston’s work illuminates the everyday practice of psychotherapists. These include (a) how the strategic ambiguity of the therapist’s stance brings, via ‘transference’, clients’ priors to light; (b) how the unstructured and negative capability of the [...] Read more.
This paper outlines the ways in which Karl Friston’s work illuminates the everyday practice of psychotherapists. These include (a) how the strategic ambiguity of the therapist’s stance brings, via ‘transference’, clients’ priors to light; (b) how the unstructured and negative capability of the therapy session reduces the salience of priors, enabling new top-down models to be forged; (c) how fostering self-reflection provides an additional step in the free energy minimization hierarchy; and (d) how Friston and Frith’s ‘duets for one’ can be conceptualized as a relational zone in which collaborative free energy minimization takes place without sacrificing complexity. Full article
(This article belongs to the Special Issue From Functional Imaging to Free Energy—Dedicated to Professor Karl Friston on the Occasion of His 65th Birthday)
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