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Déli É, Peters JF, Kisvárday Z. How the Brain Becomes the Mind: Can Thermodynamics Explain the Emergence and Nature of Emotions? ENTROPY (BASEL, SWITZERLAND) 2022; 24:1498. [PMID: 37420518 DOI: 10.3390/e24101498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/07/2022] [Accepted: 10/11/2022] [Indexed: 07/09/2023]
Abstract
The neural systems' electric activities are fundamental for the phenomenology of consciousness. Sensory perception triggers an information/energy exchange with the environment, but the brain's recurrent activations maintain a resting state with constant parameters. Therefore, perception forms a closed thermodynamic cycle. In physics, the Carnot engine is an ideal thermodynamic cycle that converts heat from a hot reservoir into work, or inversely, requires work to transfer heat from a low- to a high-temperature reservoir (the reversed Carnot cycle). We analyze the high entropy brain by the endothermic reversed Carnot cycle. Its irreversible activations provide temporal directionality for future orientation. A flexible transfer between neural states inspires openness and creativity. In contrast, the low entropy resting state parallels reversible activations, which impose past focus via repetitive thinking, remorse, and regret. The exothermic Carnot cycle degrades mental energy. Therefore, the brain's energy/information balance formulates motivation, sensed as position or negative emotions. Our work provides an analytical perspective of positive and negative emotions and spontaneous behavior from the free energy principle. Furthermore, electrical activities, thoughts, and beliefs lend themselves to a temporal organization, an orthogonal condition to physical systems. Here, we suggest that an experimental validation of the thermodynamic origin of emotions might inspire better treatment options for mental diseases.
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Affiliation(s)
- Éva Déli
- Department of Anatomy, Histology, and Embryology, University of Debrecen, 4032 Debrecen, Hungary
| | - James F Peters
- Department of Electrical & Computer Engineering, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
- Department of Mathematics, Adiyaman University, Adiyaman 02040, Turkey
| | - Zoltán Kisvárday
- Department of Anatomy, Histology, and Embryology, University of Debrecen, 4032 Debrecen, Hungary
- ELKH Neuroscience Research Group, University of Debrecen, 4032 Debrecen, Hungary
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Czégel D, Giaffar H, Tenenbaum JB, Szathmáry E. Bayes and Darwin: How replicator populations implement Bayesian computations. Bioessays 2022; 44:e2100255. [PMID: 35212408 DOI: 10.1002/bies.202100255] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 02/01/2022] [Accepted: 02/03/2022] [Indexed: 11/07/2022]
Abstract
Bayesian learning theory and evolutionary theory both formalize adaptive competition dynamics in possibly high-dimensional, varying, and noisy environments. What do they have in common and how do they differ? In this paper, we discuss structural and dynamical analogies and their limits, both at a computational and an algorithmic-mechanical level. We point out mathematical equivalences between their basic dynamical equations, generalizing the isomorphism between Bayesian update and replicator dynamics. We discuss how these mechanisms provide analogous answers to the challenge of adapting to stochastically changing environments at multiple timescales. We elucidate an algorithmic equivalence between a sampling approximation, particle filters, and the Wright-Fisher model of population genetics. These equivalences suggest that the frequency distribution of types in replicator populations optimally encodes regularities of a stochastic environment to predict future environments, without invoking the known mechanisms of multilevel selection and evolvability. A unified view of the theories of learning and evolution comes in sight.
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Affiliation(s)
- Dániel Czégel
- Institute of Evolution, Centre for Ecological Research, Budapest, Hungary.,Parmenides Foundation, Center for the Conceptual Foundations of Science, Pullach, Germany.,Doctoral School of Biology, Institute of Biology, Eötvös Loránd University, Budapest, Hungary.,Beyond Center for Fundamental Concepts in Science, Arizona State University, Tempe, Arizona, USA
| | - Hamza Giaffar
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA
| | - Joshua B Tenenbaum
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.,Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Eörs Szathmáry
- Institute of Evolution, Centre for Ecological Research, Budapest, Hungary.,Parmenides Foundation, Center for the Conceptual Foundations of Science, Pullach, Germany.,Department of Plant Systematics, Ecology and Theoretical Biology, Eötvös Loránd University, Budapest, Hungary
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