Extended mean-field theories for networks of real neurons
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912338672615424 |
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| author | Di Carlo, Luca Mignacco, Francesca Lynn, Christopher W. Bialek, William |
| author_facet | Di Carlo, Luca Mignacco, Francesca Lynn, Christopher W. Bialek, William |
| contents | If the behavior of a system with many degrees of freedom can be captured by a small number of collective variables, then plausibly there is an underlying mean-field theory. We show that simple versions of this idea fail to describe the patterns of activity in networks of real neurons. An extended mean-field theory that matches the distribution of collective variables is at least consistent, though shows signs that these networks are poised near a critical point, in agreement with other observations. These results suggest a path to analysis of emerging data on ever larger numbers of neurons. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15197 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Extended mean-field theories for networks of real neurons Di Carlo, Luca Mignacco, Francesca Lynn, Christopher W. Bialek, William Biological Physics Statistical Mechanics Neurons and Cognition If the behavior of a system with many degrees of freedom can be captured by a small number of collective variables, then plausibly there is an underlying mean-field theory. We show that simple versions of this idea fail to describe the patterns of activity in networks of real neurons. An extended mean-field theory that matches the distribution of collective variables is at least consistent, though shows signs that these networks are poised near a critical point, in agreement with other observations. These results suggest a path to analysis of emerging data on ever larger numbers of neurons. |
| title | Extended mean-field theories for networks of real neurons |
| topic | Biological Physics Statistical Mechanics Neurons and Cognition |
| url | https://arxiv.org/abs/2504.15197 |