Ising-like model replicating time-averaged spiking behaviour of in vitro neuronal networks
Abstract We analyze time-averaged experimental data from in vitro activities of neuronal networks. Through a Pairwise Maximum-Entropy method, we identify through an inverse binary Ising-like model the local fields and interaction couplings which best reproduce the average activities of each neuron a...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-55922-9 |
| _version_ | 1797233702017695744 |
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| author | Cesar I. N. Sampaio Filho Lucilla de Arcangelis Hans J. Herrmann Dietmar Plenz Patrick Kells Tiago Lins Ribeiro José S. Andrade |
| author_facet | Cesar I. N. Sampaio Filho Lucilla de Arcangelis Hans J. Herrmann Dietmar Plenz Patrick Kells Tiago Lins Ribeiro José S. Andrade |
| author_sort | Cesar I. N. Sampaio Filho |
| collection | DOAJ |
| description | Abstract We analyze time-averaged experimental data from in vitro activities of neuronal networks. Through a Pairwise Maximum-Entropy method, we identify through an inverse binary Ising-like model the local fields and interaction couplings which best reproduce the average activities of each neuron as well as the statistical correlations between the activities of each pair of neurons in the system. The specific information about the type of neurons is mainly stored in the local fields, while a symmetric distribution of interaction constants seems generic. Our findings demonstrate that, despite not being directly incorporated into the inference approach, the experimentally observed correlations among groups of three neurons are accurately captured by the derived Ising-like model. Within the context of the thermodynamic analogy inherent to the Ising-like models developed in this study, our findings additionally indicate that these models demonstrate characteristics of second-order phase transitions between ferromagnetic and paramagnetic states at temperatures above, but close to, unity. Considering that the operating temperature utilized in the Maximum-Entropy method is $$T_{o}=1$$ T o = 1 , this observation further expands the thermodynamic conceptual parallelism postulated in this work for the manifestation of criticality in neuronal network behavior. |
| first_indexed | 2024-04-24T16:20:22Z |
| format | Article |
| id | doaj.art-d914c76a577a42e2b702ac5b7979548a |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T16:20:22Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-d914c76a577a42e2b702ac5b7979548a2024-03-31T11:16:25ZengNature PortfolioScientific Reports2045-23222024-03-011411810.1038/s41598-024-55922-9Ising-like model replicating time-averaged spiking behaviour of in vitro neuronal networksCesar I. N. Sampaio Filho0Lucilla de Arcangelis1Hans J. Herrmann2Dietmar Plenz3Patrick Kells4Tiago Lins Ribeiro5José S. Andrade6Departamento de Física, Universidade Federal do CearáDepartment of Mathematics and Physics, University of Campania “Luigi Vanvitelli”Departamento de Física, Universidade Federal do CearáSection on Critical Brain Dynamics, NIMHSection on Critical Brain Dynamics, NIMHSection on Critical Brain Dynamics, NIMHDepartamento de Física, Universidade Federal do CearáAbstract We analyze time-averaged experimental data from in vitro activities of neuronal networks. Through a Pairwise Maximum-Entropy method, we identify through an inverse binary Ising-like model the local fields and interaction couplings which best reproduce the average activities of each neuron as well as the statistical correlations between the activities of each pair of neurons in the system. The specific information about the type of neurons is mainly stored in the local fields, while a symmetric distribution of interaction constants seems generic. Our findings demonstrate that, despite not being directly incorporated into the inference approach, the experimentally observed correlations among groups of three neurons are accurately captured by the derived Ising-like model. Within the context of the thermodynamic analogy inherent to the Ising-like models developed in this study, our findings additionally indicate that these models demonstrate characteristics of second-order phase transitions between ferromagnetic and paramagnetic states at temperatures above, but close to, unity. Considering that the operating temperature utilized in the Maximum-Entropy method is $$T_{o}=1$$ T o = 1 , this observation further expands the thermodynamic conceptual parallelism postulated in this work for the manifestation of criticality in neuronal network behavior.https://doi.org/10.1038/s41598-024-55922-9 |
| spellingShingle | Cesar I. N. Sampaio Filho Lucilla de Arcangelis Hans J. Herrmann Dietmar Plenz Patrick Kells Tiago Lins Ribeiro José S. Andrade Ising-like model replicating time-averaged spiking behaviour of in vitro neuronal networks Scientific Reports |
| title | Ising-like model replicating time-averaged spiking behaviour of in vitro neuronal networks |
| title_full | Ising-like model replicating time-averaged spiking behaviour of in vitro neuronal networks |
| title_fullStr | Ising-like model replicating time-averaged spiking behaviour of in vitro neuronal networks |
| title_full_unstemmed | Ising-like model replicating time-averaged spiking behaviour of in vitro neuronal networks |
| title_short | Ising-like model replicating time-averaged spiking behaviour of in vitro neuronal networks |
| title_sort | ising like model replicating time averaged spiking behaviour of in vitro neuronal networks |
| url | https://doi.org/10.1038/s41598-024-55922-9 |
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