Statistical mechanics for networks of real neurons
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912010182066176 |
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| author | Meshulam, Leenoy Bialek, William |
| author_facet | Meshulam, Leenoy Bialek, William |
| contents | Perceptions and actions, thoughts and memories result from coordinated activity in hundreds or even thousands of neurons in the brain. It is an old dream of the physics community to provide a statistical mechanics description for these and other emergent phenomena of life. These aspirations appear in a new light because of developments in our ability to measure the electrical activity of the brain, sampling thousands of individual neurons simultaneously over hours or days. We review the progress that has been made in bringing theory and experiment together, focusing on maximum entropy methods and a phenomenological renormalization group. These approaches have uncovered new, quantitatively reproducible collective behaviors in networks of real neurons, and provide examples of rich parameter--free predictions that agree in detail with experiment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00412 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Statistical mechanics for networks of real neurons Meshulam, Leenoy Bialek, William Disordered Systems and Neural Networks Neurons and Cognition Perceptions and actions, thoughts and memories result from coordinated activity in hundreds or even thousands of neurons in the brain. It is an old dream of the physics community to provide a statistical mechanics description for these and other emergent phenomena of life. These aspirations appear in a new light because of developments in our ability to measure the electrical activity of the brain, sampling thousands of individual neurons simultaneously over hours or days. We review the progress that has been made in bringing theory and experiment together, focusing on maximum entropy methods and a phenomenological renormalization group. These approaches have uncovered new, quantitatively reproducible collective behaviors in networks of real neurons, and provide examples of rich parameter--free predictions that agree in detail with experiment. |
| title | Statistical mechanics for networks of real neurons |
| topic | Disordered Systems and Neural Networks Neurons and Cognition |
| url | https://arxiv.org/abs/2409.00412 |