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Deep Intelligence: What AI Should Learn from Nature’s Imagination. Cognit Comput 2023. [DOI: 10.1007/s12559-023-10124-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
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Yufik YM, Friston KJ, Moran RJ. Editorial: Understanding in the human and the machine. Front Syst Neurosci 2022; 16:1081112. [PMID: 36506866 PMCID: PMC9732658 DOI: 10.3389/fnsys.2022.1081112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 11/02/2022] [Indexed: 11/27/2022] Open
Affiliation(s)
- Yan M. Yufik
- Virtual Structures Research Inc., Potomac, MD, United States,*Correspondence: Yan M. Yufik
| | - Karl J. Friston
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Rosalyn J. Moran
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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Kroger J, Kim C. Frontopolar Cortex Specializes for Manipulation of Structured Information. Front Syst Neurosci 2022; 16:788395. [PMID: 35308567 PMCID: PMC8924948 DOI: 10.3389/fnsys.2022.788395] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 01/20/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- James Kroger
- Department of Psychology, New Mexico State University, Las Cruces, NM, United States
| | - Chobok Kim
- Department of Psychology, College of Social Sciences, Kyungpook National University, Daegu, South Korea
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Vicencio-Jimenez S, Villalobos M, Maldonado PE, Vergara RC. The Energy Homeostasis Principle: A Naturalistic Approach to Explain the Emergence of Behavior. Front Syst Neurosci 2022; 15:782781. [PMID: 35069133 PMCID: PMC8770284 DOI: 10.3389/fnsys.2021.782781] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 12/13/2021] [Indexed: 11/13/2022] Open
Abstract
It is still elusive to explain the emergence of behavior and understanding based on its neural mechanisms. One renowned proposal is the Free Energy Principle (FEP), which uses an information-theoretic framework derived from thermodynamic considerations to describe how behavior and understanding emerge. FEP starts from a whole-organism approach, based on mental states and phenomena, mapping them into the neuronal substrate. An alternative approach, the Energy Homeostasis Principle (EHP), initiates a similar explanatory effort but starts from single-neuron phenomena and builds up to whole-organism behavior and understanding. In this work, we further develop the EHP as a distinct but complementary vision to FEP and try to explain how behavior and understanding would emerge from the local requirements of the neurons. Based on EHP and a strict naturalist approach that sees living beings as physical and deterministic systems, we explain scenarios where learning would emerge without the need for volition or goals. Given these starting points, we state several considerations of how we see the nervous system, particularly the role of the function, purpose, and conception of goal-oriented behavior. We problematize these conceptions, giving an alternative teleology-free framework in which behavior and, ultimately, understanding would still emerge. We reinterpret neural processing by explaining basic learning scenarios up to simple anticipatory behavior. Finally, we end the article with an evolutionary perspective of how this non-goal-oriented behavior appeared. We acknowledge that our proposal, in its current form, is still far from explaining the emergence of understanding. Nonetheless, we set the ground for an alternative neuron-based framework to ultimately explain understanding.
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Affiliation(s)
- Sergio Vicencio-Jimenez
- The Center for Hearing and Balance, Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Mario Villalobos
- Escuela de Psicología y Filosofía, Universidad de Tarapacá, Arica, Chile
| | - Pedro E. Maldonado
- Laboratorio de Neurosistemas, Departamento de Neurociencia & BNI, Facultad de Medicina, Universidad de Chile, Santiago, Chile
| | - Rodrigo C. Vergara
- Departamento de Kinesiología, Facultad de Artes y Educación Física, Universidad Metropolitana de las Ciencias de la Educación, Ñuñoa, Chile
- *Correspondence: Rodrigo C. Vergara
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Yufik Y, Malhotra R. Situational Understanding in the Human and the Machine. Front Syst Neurosci 2021; 15:786252. [PMID: 35002643 PMCID: PMC8733725 DOI: 10.3389/fnsys.2021.786252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 11/24/2021] [Indexed: 11/13/2022] Open
Abstract
The Air Force research programs envision developing AI technologies that will ensure battlespace dominance, by radical increases in the speed of battlespace understanding and decision-making. In the last half century, advances in AI have been concentrated in the area of machine learning. Recent experimental findings and insights in systems neuroscience, the biophysics of cognition, and other disciplines provide converging results that set the stage for technologies of machine understanding and machine-augmented Situational Understanding. This paper will review some of the key ideas and results in the literature, and outline new suggestions. We define situational understanding and the distinctions between understanding and awareness, consider examples of how understanding-or lack of it-manifest in performance, and review hypotheses concerning the underlying neuronal mechanisms. Suggestions for further R&D are motivated by these hypotheses and are centered on the notions of Active Inference and Virtual Associative Networks.
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Affiliation(s)
- Yan Yufik
- Virtual Structures Research, Inc., Potomac, MD, United States
| | - Raj Malhotra
- United States Air Force Sensor Directorate, Dayton, OH, United States
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Kozma R, Baars BJ, Geld N. Evolutionary Advantages of Stimulus-Driven EEG Phase Transitions in the Upper Cortical Layers. Front Syst Neurosci 2021; 15:784404. [PMID: 34955771 PMCID: PMC8692947 DOI: 10.3389/fnsys.2021.784404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 11/03/2021] [Indexed: 11/13/2022] Open
Abstract
Spatio-temporal brain activity monitored by EEG recordings in humans and other mammals has identified beta/gamma oscillations (20-80 Hz), which are self-organized into spatio-temporal structures recurring at theta/alpha rates (4-12 Hz). These structures have statistically significant correlations with sensory stimuli and reinforcement contingencies perceived by the subject. The repeated collapse of self-organized structures at theta/alpha rates generates laterally propagating phase gradients (phase cones), ignited at some specific location of the cortical sheet. Phase cones have been interpreted as neural signatures of transient perceptual experiences according to the cinematic theory of brain dynamics. The rapid expansion of essentially isotropic phase cones is consistent with the propagation of perceptual broadcasts postulated by Global Workspace Theory (GWT). What is the evolutionary advantage of brains operating with repeatedly collapsing dynamics? This question is answered using thermodynamic concepts. According to neuropercolation theory, waking brains are described as non-equilibrium thermodynamic systems operating at the edge of criticality, undergoing repeated phase transitions. This work analyzes the role of long-range axonal connections and metabolic processes in the regulation of critical brain dynamics. Historically, the near 10 Hz domain has been associated with conscious sensory integration, cortical "ignitions" linked to conscious visual perception, and conscious experiences. We can therefore combine a very large body of experimental evidence and theory, including graph theory, neuropercolation, and GWT. This cortical operating style may optimize a tradeoff between rapid adaptation to novelty vs. stable and widespread self-organization, therefore resulting in significant Darwinian benefits.
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Affiliation(s)
- Robert Kozma
- Center for Large-Scale Intelligent Optimization and Networks, Department of Mathematics, University of Memphis, Memphis, TN, United States
| | - Bernard J. Baars
- Center for the Future Mind, Florida Atlantic University, Boca Raton, FL, United States
- Society for MindBrain Sciences, San Diego, CA, United States
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Latash ML. Understanding and Synergy: A Single Concept at Different Levels of Analysis? Front Syst Neurosci 2021; 15:735406. [PMID: 34867220 PMCID: PMC8636674 DOI: 10.3389/fnsys.2021.735406] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 11/01/2021] [Indexed: 11/15/2022] Open
Abstract
Biological systems differ from the inanimate world in their behaviors ranging from simple movements to coordinated purposeful actions by large groups of muscles, to perception of the world based on signals of different modalities, to cognitive acts, and to the role of self-imposed constraints such as laws of ethics. Respectively, depending on the behavior of interest, studies of biological objects based on laws of nature (physics) have to deal with different salient sets of variables and parameters. Understanding is a high-level concept, and its analysis has been linked to other high-level concepts such as "mental model" and "meaning". Attempts to analyze understanding based on laws of nature are an example of the top-down approach. Studies of the neural control of movements represent an opposite, bottom-up approach, which starts at the interface with classical physics of the inanimate world and operates with traditional concepts such as forces, coordinates, etc. There are common features shared by the two approaches. In particular, both assume organizations of large groups of elements into task-specific groups, which can be described with only a handful of salient variables. Both assume optimality criteria that allow the emergence of families of solutions to typical tasks. Both assume predictive processes reflected in anticipatory adjustments to actions (motor and non-motor). Both recognize the importance of generating dynamically stable solutions. The recent progress in studies of the neural control of movements has led to a theory of hierarchical control with spatial referent coordinates for the effectors. This theory, in combination with the uncontrolled manifold hypothesis, allows quantifying the stability of actions with respect to salient variables. This approach has been used in the analysis of motor learning, changes in movements with typical and atypical development and with aging, and impaired actions by patients with various neurological disorders. It has been developed to address issues of kinesthetic perception. There seems to be hope that the two counter-directional approaches will meet and result in a single theoretical scheme encompassing biological phenomena from figuring out the best next move in a chess position to activating motor units appropriate for implementing that move on the chessboard.
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Affiliation(s)
- Mark L. Latash
- Department of Kinesiology, The Pennsylvania State University, University Park, PA, United States
- Moscow Institute of Physics and Technology, Dolgoprudnyj, Russia
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Fingelkurts AA, Fingelkurts AA, Kallio-Tamminen T. Self, Me and I in the repertoire of spontaneously occurring altered states of Selfhood: eight neurophenomenological case study reports. Cogn Neurodyn 2021; 16:255-282. [PMID: 35401860 PMCID: PMC8934794 DOI: 10.1007/s11571-021-09719-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 08/20/2021] [Accepted: 09/02/2021] [Indexed: 10/20/2022] Open
Abstract
This study investigates eight case reports of spontaneously emerging, brief episodes of vivid altered states of Selfhood (ASoSs) that occurred during mental exercise in six long-term meditators by using a neurophenomenological electroencephalography (EEG) approach. In agreement with the neurophenomenological methodology, first-person reports were used to identify such spontaneous ASoSs and to guide the neural analysis, which involved the estimation of three operational modules of the brain self-referential network (measured by EEG operational synchrony). The result of such analysis demonstrated that the documented ASoSs had unique neurophenomenological profiles, where several aspects or components of Selfhood (measured neurophysiologically and phenomenologically) are affected and expressed differently, but still in agreement with the neurophysiological three-dimensional construct model of the complex experiential Selfhood proposed in our earlier work (Fingelkurts et al. in Conscious Cogn 86:103031. 10.1016/j.concog.2020.103031, 2020).
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Wang J, Wu X, Li M. Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer's Disease. ENTROPY (BASEL, SWITZERLAND) 2021; 23:216. [PMID: 33579012 PMCID: PMC7916760 DOI: 10.3390/e23020216] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 02/03/2021] [Accepted: 02/08/2021] [Indexed: 12/22/2022]
Abstract
This paper seeks to advance the state-of-the-art in analysing fMRI data to detect onset of Alzheimer's disease and identify stages in the disease progression. We employ methods of network neuroscience to represent correlation across fMRI data arrays, and introduce novel techniques for network construction and analysis. In network construction, we vary thresholds in establishing BOLD time series correlation between nodes, yielding variations in topological and other network characteristics. For network analysis, we employ methods developed for modelling statistical ensembles of virtual particles in thermal systems. The microcanonical ensemble and the canonical ensemble are analogous to two different fMRI network representations. In the former case, there is zero variance in the number of edges in each network, while in the latter case the set of networks have a variance in the number of edges. Ensemble methods describe the macroscopic properties of a network by considering the underlying microscopic characterisations which are in turn closely related to the degree configuration and network entropy. When applied to fMRI data in populations of Alzheimer's patients and controls, our methods demonstrated levels of sensitivity adequate for clinical purposes in both identifying brain regions undergoing pathological changes and in revealing the dynamics of such changes.
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Affiliation(s)
- Jianjia Wang
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China;
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
| | - Xichen Wu
- School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China;
| | - Mingrui Li
- Department of Computer Science, University of York, York YO10 5GH, UK;
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Ramstead MJD, Hesp C, Tschantz A, Smith R, Constant A, Friston K. Neural and phenotypic representation under the free-energy principle. Neurosci Biobehav Rev 2021; 120:109-122. [PMID: 33271162 PMCID: PMC7955287 DOI: 10.1016/j.neubiorev.2020.11.024] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 11/19/2020] [Accepted: 11/27/2020] [Indexed: 01/19/2023]
Abstract
The aim of this paper is to leverage the free-energy principle and its corollary process theory, active inference, to develop a generic, generalizable model of the representational capacities of living creatures; that is, a theory of phenotypic representation. Given their ubiquity, we are concerned with distributed forms of representation (e.g., population codes), whereby patterns of ensemble activity in living tissue come to represent the causes of sensory input or data. The active inference framework rests on the Markov blanket formalism, which allows us to partition systems of interest, such as biological systems, into internal states, external states, and the blanket (active and sensory) states that render internal and external states conditionally independent of each other. In this framework, the representational capacity of living creatures emerges as a consequence of their Markovian structure and nonequilibrium dynamics, which together entail a dual-aspect information geometry. This entails a modest representational capacity: internal states have an intrinsic information geometry that describes their trajectory over time in state space, as well as an extrinsic information geometry that allows internal states to encode (the parameters of) probabilistic beliefs about (fictive) external states. Building on this, we describe here how, in an automatic and emergent manner, information about stimuli can come to be encoded by groups of neurons bound by a Markov blanket; what is known as the neuronal packet hypothesis. As a concrete demonstration of this type of emergent representation, we present numerical simulations showing that self-organizing ensembles of active inference agents sharing the right kind of probabilistic generative model are able to encode recoverable information about a stimulus array.
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Affiliation(s)
- Maxwell J D Ramstead
- Division of Social and Transcultural Psychiatry, Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Culture, Mind, and Brain Program, McGill University, Montreal, Quebec, Canada; Wellcome Centre for Human Neuroimaging, University College London, London, WC1N3BG, UK.
| | - Casper Hesp
- Wellcome Centre for Human Neuroimaging, University College London, London, WC1N3BG, UK; Department of Psychology, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands; Amsterdam Brain and Cognition Centre, University of Amsterdam, Science Park 904, 1098 XH, Amsterdam, the Netherlands; Institute for Advanced Study, University of Amsterdam, Oude Turfmarkt 147, 1012 GC Amsterdam, the Netherlands.
| | - Alexander Tschantz
- Sackler Centre for Consciousness Science, University of Sussex, Brighton, UK; Department of Informatics, University of Sussex, Brighton, UK.
| | - Ryan Smith
- Laureate Institute for Brain Research, Tulsa, OK, USA.
| | - Axel Constant
- Culture, Mind, and Brain Program, McGill University, Montreal, Quebec, Canada; Wellcome Centre for Human Neuroimaging, University College London, London, WC1N3BG, UK; Theory and Method in Biosciences, Level 6, Charles Perkins Centre D17, Johns Hopkins Drive, University of Sydney, NSW, 2006, Australia.
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, University College London, London, WC1N3BG, UK.
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Friston KJ, Parr T, Yufik Y, Sajid N, Price CJ, Holmes E. Generative models, linguistic communication and active inference. Neurosci Biobehav Rev 2020; 118:42-64. [PMID: 32687883 PMCID: PMC7758713 DOI: 10.1016/j.neubiorev.2020.07.005] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 06/26/2020] [Accepted: 07/08/2020] [Indexed: 11/24/2022]
Abstract
This paper presents a biologically plausible generative model and inference scheme that is capable of simulating communication between synthetic subjects who 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 linguistic communication 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 linguistic communication 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.
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Affiliation(s)
- Karl J Friston
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, London, WC1N 3AR, UK.
| | - Thomas Parr
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, London, WC1N 3AR, UK.
| | - Yan Yufik
- Virtual Structures Research, Inc., 12204 Saint James Rd, Potomac, MD 20854, USA.
| | - Noor Sajid
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, London, WC1N 3AR, UK.
| | - Catherine J Price
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, London, WC1N 3AR, UK.
| | - Emma Holmes
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, 12 Queen Square, London, WC1N 3AR, UK.
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12
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Laws of nature that define biological action and perception. Phys Life Rev 2020; 36:47-67. [PMID: 32868159 DOI: 10.1016/j.plrev.2020.07.007] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 07/29/2020] [Indexed: 12/12/2022]
Abstract
We describe a physical approach to biological functions, with the emphasis on the motor and sensory functions. The approach assumes the existence of biology-specific laws of nature uniting salient physical variables and parameters. In contrast to movements in inanimate nature, actions are produced by changes in parameters of the corresponding laws of nature. For movements, parameters are associated with spatial referent coordinates (RCs) for the effectors. Stability of motor actions is ensured by the abundant mapping of RCs across hierarchical control levels. The sensory function is viewed as based on an interaction of efferent and afferent signals leading to an iso-perceptual manifold where percepts of salient sensory variables are stable. This approach offers novel interpretations for a variety of known neurophysiological and behavioral phenomena and makes a number of novel testable predictions. In particular, we discuss novel interpretations for the well-known phenomena of agonist-antagonist co-activation and vibration-induced illusions of both position and force. We also interpret results of several new experiments with unintentional force changes and with analysis of accuracy of perception of variables produced by elements of multi-element systems. Recently, this approach has been expanded to interpret motor disorders including spasticity and consequences of subcortical disorders (such as Parkinson's disease). We suggest that the approach can be developed for cognitive functions.
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