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Kim CS. Free energy and inference in living systems. Interface Focus 2023; 13:20220041. [PMID: 37065269 PMCID: PMC10102732 DOI: 10.1098/rsfs.2022.0041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 01/18/2023] [Indexed: 04/18/2023] Open
Abstract
Organisms are non-equilibrium, stationary systems self-organized via spontaneous symmetry breaking and undergoing metabolic cycles with broken detailed balance in the environment. The thermodynamic free-energy (FE) principle describes an organism's homeostasis as the regulation of biochemical work constrained by the physical FE cost. By contrast, recent research in neuroscience and theoretical biology explains a higher organism's homeostasis and allostasis as Bayesian inference facilitated by the informational FE. As an integrated approach to living systems, this study presents an FE minimization theory overarching the essential features of both the thermodynamic and neuroscientific FE principles. Our results reveal that the perception and action of animals result from active inference entailed by FE minimization in the brain, and the brain operates as a Schrödinger's machine conducting the neural mechanics of minimizing sensory uncertainty. A parsimonious model suggests that the Bayesian brain develops the optimal trajectories in neural manifolds and induces a dynamic bifurcation between neural attractors in the process of active inference.
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Affiliation(s)
- Chang Sub Kim
- Department of Physics, Chonnam National University, Gwangju 61186, Republic of Korea
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2
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Fan Zhang Y, Mameri S, Xie T, Sadoun A. Local similarity of activity patterns during auditory and visual processing. Neurosci Lett 2022; 790:136891. [PMID: 36181962 DOI: 10.1016/j.neulet.2022.136891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 09/20/2022] [Accepted: 09/26/2022] [Indexed: 11/29/2022]
Abstract
Neuroimaging studies have shown that brain activity is variable and changes according to stimuli and the environmental context, reflecting brain coding or information representations at different processing levels. However, little is known about activity organization that reflects coding strategies. Here, we explored and compared two different coding approaches, spatial via cross-correlation and intensity-based coding using mutual information. Using two fMRI datasets and different seeds, we searched for the spatial and intensity-based similarities with the seeds in brain activity. Our results showed that, apart from the seed regions, significant regions detected by intensity-based similarity analysis differ completely from those found using cross-correlation. These findings may indicate that information shared through spatial coding differs from that transmitted via non-spatial coding processes. Our results suggest that brain coding is organized in several different ways to optimize information processing.
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Affiliation(s)
- Yi Fan Zhang
- UMR 5549, Université de Toulouse 3, France, Centre National de la Recherche Scientifique, Toulouse, France; Centre de Recherche Cerveau et Cognition, Université de Toulouse 3, Université Paul Sabatier, Toulouse, France.
| | - Samir Mameri
- University of Bordj Bou Arreridj, Algeria; Laboratory of theoretical physics (LPT), University of Béjaïa, Algeria
| | - Ting Xie
- Centre de Recherches en Cancérologie de Toulouse (CRCT), INSERM U1037, Toulouse 31037, France; Université Paul Sabatier III, Toulouse 31400, Toulouse, France
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Sadoun A, Chauhan T, Zhang YF, Gallois Y, Marx M, Deguine O, Barone P, Strelnikov K. Intensity patterns at the peaks of brain activity in fMRI and PET are highly correlated with neural models of spatial integration. Eur J Neurosci 2021; 54:7141-7151. [PMID: 34550613 PMCID: PMC9291889 DOI: 10.1111/ejn.15469] [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: 01/25/2021] [Revised: 09/12/2021] [Accepted: 09/15/2021] [Indexed: 11/30/2022]
Abstract
Spatial integration during the brain's cognitive activity prompts changes in energy used by different neuroglial populations. Nevertheless, the organisation of such integration in 3D ‐brain activity remains undescribed from a quantitative standpoint. In response, we applied a cross‐correlation between brain activity and integrative models, which yielded a deeper understanding of information integration in functional brain mapping. We analysed four datasets obtained via fundamentally different neuroimaging techniques (functional magnetic resonance imaging [fMRI] and positron emission tomography [PET]) and found that models of spatial integration with an increasing input to each step of integration were significantly more correlated with brain activity than models with a constant input to each step of integration. In addition, marking the voxels with the maximal correlation, we found exceptionally high intersubject consistency with the initial brain activity at the peaks. Our method demonstrated for the first time that the network of peaks of brain activity is organised strictly according to the models of spatial integration independent of neuroimaging techniques. The highest correlation with models integrating an increasing at each step input suggests that brain activity reflects a network of integrative processes where the results of integration in some neuroglial populations serve as an input to other neuroglial populations.
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Affiliation(s)
- Amirouche Sadoun
- UMR 5549, Centre National de la Recherche Scientifique, Université de Toulouse 3, Toulouse, France.,Centre de Recherche Cerveau et Cognition, Université de Toulouse 3, Université Paul Sabatier, Toulouse, France
| | - Tushar Chauhan
- UMR 5549, Centre National de la Recherche Scientifique, Université de Toulouse 3, Toulouse, France.,Centre de Recherche Cerveau et Cognition, Université de Toulouse 3, Université Paul Sabatier, Toulouse, France
| | - Yi Fan Zhang
- UMR 5549, Centre National de la Recherche Scientifique, Université de Toulouse 3, Toulouse, France.,Centre de Recherche Cerveau et Cognition, Université de Toulouse 3, Université Paul Sabatier, Toulouse, France
| | - Yohan Gallois
- Service d'Oto-Rhino-Laryngologie et Oto-Neurologie, CHU de Toulouse, Université de Toulouse 3, Toulouse, France
| | - Mathieu Marx
- Service d'Oto-Rhino-Laryngologie et Oto-Neurologie, CHU de Toulouse, Université de Toulouse 3, Toulouse, France
| | - Olivier Deguine
- UMR 5549, Centre National de la Recherche Scientifique, Université de Toulouse 3, Toulouse, France.,Centre de Recherche Cerveau et Cognition, Université de Toulouse 3, Université Paul Sabatier, Toulouse, France.,Service d'Oto-Rhino-Laryngologie et Oto-Neurologie, CHU de Toulouse, Université de Toulouse 3, Toulouse, France
| | - Pascal Barone
- UMR 5549, Centre National de la Recherche Scientifique, Université de Toulouse 3, Toulouse, France.,Centre de Recherche Cerveau et Cognition, Université de Toulouse 3, Université Paul Sabatier, Toulouse, France
| | - Kuzma Strelnikov
- UMR 5549, Centre National de la Recherche Scientifique, Université de Toulouse 3, Toulouse, France.,Centre de Recherche Cerveau et Cognition, Université de Toulouse 3, Université Paul Sabatier, Toulouse, France.,Service d'Oto-Rhino-Laryngologie et Oto-Neurologie, CHU de Toulouse, Université de Toulouse 3, Toulouse, France
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Chen Y, Zhang J. How Energy Supports Our Brain to Yield Consciousness: Insights From Neuroimaging Based on the Neuroenergetics Hypothesis. Front Syst Neurosci 2021; 15:648860. [PMID: 34295226 PMCID: PMC8291083 DOI: 10.3389/fnsys.2021.648860] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Accepted: 05/26/2021] [Indexed: 11/13/2022] Open
Abstract
Consciousness is considered a result of specific neuronal processes and mechanisms in the brain. Various suggested neuronal mechanisms, including the information integration theory (IIT), global neuronal workspace theory (GNWS), and neuronal construction of time and space as in the context of the temporospatial theory of consciousness (TTC), have been laid forth. However, despite their focus on different neuronal mechanisms, these theories neglect the energetic-metabolic basis of the neuronal mechanisms that are supposed to yield consciousness. Based on the findings of physiology-induced (sleep), pharmacology-induced (general anesthesia), and pathology-induced [vegetative state/unresponsive wakeful syndrome (VS/UWS)] loss of consciousness in both human subjects and animals, we, in this study, suggest that the energetic-metabolic processes focusing on ATP, glucose, and γ-aminobutyrate/glutamate are indispensable for functional connectivity (FC) of normal brain networks that renders consciousness possible. Therefore, we describe the energetic-metabolic predispositions of consciousness (EPC) that complement the current theories focused on the neural correlates of consciousness (NCC).
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Affiliation(s)
- Yali Chen
- Department of Anesthesiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Jun Zhang
- Department of Anesthesiology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical college, Fudan University, Shanghai, China
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Kim CS. Bayesian mechanics of perceptual inference and motor control in the brain. BIOLOGICAL CYBERNETICS 2021; 115:87-102. [PMID: 33471182 PMCID: PMC7925488 DOI: 10.1007/s00422-021-00859-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 01/06/2021] [Indexed: 06/12/2023]
Abstract
The free energy principle (FEP) in the neurosciences stipulates that all viable agents induce and minimize informational free energy in the brain to fit their environmental niche. In this study, we continue our effort to make the FEP a more physically principled formalism by implementing free energy minimization based on the principle of least action. We build a Bayesian mechanics (BM) by casting the formulation reported in the earlier publication (Kim in Neural Comput 30:2616-2659, 2018, https://doi.org/10.1162/neco_a_01115 ) to considering active inference beyond passive perception. The BM is a neural implementation of variational Bayes under the FEP in continuous time. The resulting BM is provided as an effective Hamilton's equation of motion and subject to the control signal arising from the brain's prediction errors at the proprioceptive level. To demonstrate the utility of our approach, we adopt a simple agent-based model and present a concrete numerical illustration of the brain performing recognition dynamics by integrating BM in neural phase space. Furthermore, we recapitulate the major theoretical architectures in the FEP by comparing our approach with the common state-space formulations.
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Affiliation(s)
- Chang Sub Kim
- Department of Physics, Chonnam National University, Gwangju, 61186, Republic of Korea.
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Sadoun A, Chauhan T, Mameri S, Zhang Y, Barone P, Deguine O, Strelnikov K. Stimulus-specific information is represented as local activity patterns across the brain. Neuroimage 2020; 223:117326. [PMID: 32882381 DOI: 10.1016/j.neuroimage.2020.117326] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 08/21/2020] [Accepted: 08/25/2020] [Indexed: 12/12/2022] Open
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