Modeling of a Neural System Based on Statistical Mechanics
The minimization of a free energy is often regarded as the key principle in understanding how the brain works and how the brain structure forms. In particular, a statistical-mechanics-based neural network model is expected to allow one to interpret many aspects of the neural firing and learning proc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-11-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/20/11/848 |
| _version_ | 1798041849110200320 |
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| author | Myoung Won Cho Moo Young Choi |
| author_facet | Myoung Won Cho Moo Young Choi |
| author_sort | Myoung Won Cho |
| collection | DOAJ |
| description | The minimization of a free energy is often regarded as the key principle in understanding how the brain works and how the brain structure forms. In particular, a statistical-mechanics-based neural network model is expected to allow one to interpret many aspects of the neural firing and learning processes in terms of general concepts and mechanisms in statistical physics. Nevertheless, the definition of the free energy in a neural system is usually an intricate problem without an evident solution. After the pioneering work by Hopfield, several statistical-mechanics-based models have suggested a variety of definition of the free energy or the entropy in a neural system. Among those, the Feynman machine, proposed recently, presents the free energy of a neural system defined via the Feynman path integral formulation with the explicit time variable. In this study, we first give a brief review of the previous relevant models, paying attention to the troublesome problems in them, and examine how the Feynman machine overcomes several vulnerable points in previous models and derives the outcome of the firing or the learning rule in a (biological) neural system as the extremum state in the free energy. Specifically, the model reveals that the biological learning mechanism, called spike-timing-dependent plasticity, relates to the free-energy minimization principle. Basically, computing and learning mechanisms in the Feynman machine base on the exact spike timings of neurons, such as those in a biological neural system. We discuss the consequence of the adoption of an explicit time variable in modeling a neural system and the application of the free-energy minimization principle to understanding the phenomena in the brain. |
| first_indexed | 2024-04-11T22:28:24Z |
| format | Article |
| id | doaj.art-57208754d7034e3fa1caa3f29d420f23 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T22:28:24Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-57208754d7034e3fa1caa3f29d420f232022-12-22T03:59:36ZengMDPI AGEntropy1099-43002018-11-01201184810.3390/e20110848e20110848Modeling of a Neural System Based on Statistical MechanicsMyoung Won Cho0Moo Young Choi1Department of Global Medical Science, Sungshin Women’s University, Seoul 01133, KoreaDepartment of Physics and Astronomy and Center for Theoretical Physics, Seoul National University, Seoul 08826, KoreaThe minimization of a free energy is often regarded as the key principle in understanding how the brain works and how the brain structure forms. In particular, a statistical-mechanics-based neural network model is expected to allow one to interpret many aspects of the neural firing and learning processes in terms of general concepts and mechanisms in statistical physics. Nevertheless, the definition of the free energy in a neural system is usually an intricate problem without an evident solution. After the pioneering work by Hopfield, several statistical-mechanics-based models have suggested a variety of definition of the free energy or the entropy in a neural system. Among those, the Feynman machine, proposed recently, presents the free energy of a neural system defined via the Feynman path integral formulation with the explicit time variable. In this study, we first give a brief review of the previous relevant models, paying attention to the troublesome problems in them, and examine how the Feynman machine overcomes several vulnerable points in previous models and derives the outcome of the firing or the learning rule in a (biological) neural system as the extremum state in the free energy. Specifically, the model reveals that the biological learning mechanism, called spike-timing-dependent plasticity, relates to the free-energy minimization principle. Basically, computing and learning mechanisms in the Feynman machine base on the exact spike timings of neurons, such as those in a biological neural system. We discuss the consequence of the adoption of an explicit time variable in modeling a neural system and the application of the free-energy minimization principle to understanding the phenomena in the brain.https://www.mdpi.com/1099-4300/20/11/848neural network modelstatistical mechanicsfree-energy minimization principle |
| spellingShingle | Myoung Won Cho Moo Young Choi Modeling of a Neural System Based on Statistical Mechanics Entropy neural network model statistical mechanics free-energy minimization principle |
| title | Modeling of a Neural System Based on Statistical Mechanics |
| title_full | Modeling of a Neural System Based on Statistical Mechanics |
| title_fullStr | Modeling of a Neural System Based on Statistical Mechanics |
| title_full_unstemmed | Modeling of a Neural System Based on Statistical Mechanics |
| title_short | Modeling of a Neural System Based on Statistical Mechanics |
| title_sort | modeling of a neural system based on statistical mechanics |
| topic | neural network model statistical mechanics free-energy minimization principle |
| url | https://www.mdpi.com/1099-4300/20/11/848 |
| work_keys_str_mv | AT myoungwoncho modelingofaneuralsystembasedonstatisticalmechanics AT mooyoungchoi modelingofaneuralsystembasedonstatisticalmechanics |