Statistical Field Theory and Neural Structures Dynamics IV: Field-Theoretic Formalism for Interacting Collective States
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909207479975936 |
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| author | Gosselin, Pierre Lotz, Aïleen |
| author_facet | Gosselin, Pierre Lotz, Aïleen |
| contents | Building upon the findings presented in the first three papers of this series, we formulate an effective field theory for interacting collective states. These states consist of a large number of interconnected neurons and are distinguished by their intrinsic activity. The field theory encompasses an infinite set of fields, each of which characterizes the dynamics of a specific type of collective state. Interaction terms within the theory drive transitions between various collective states, allowing us to describe processes such as activation, association, and deactivation of these states. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_18417 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Statistical Field Theory and Neural Structures Dynamics IV: Field-Theoretic Formalism for Interacting Collective States Gosselin, Pierre Lotz, Aïleen Biological Physics Statistical Mechanics High Energy Physics - Theory Neurons and Cognition Building upon the findings presented in the first three papers of this series, we formulate an effective field theory for interacting collective states. These states consist of a large number of interconnected neurons and are distinguished by their intrinsic activity. The field theory encompasses an infinite set of fields, each of which characterizes the dynamics of a specific type of collective state. Interaction terms within the theory drive transitions between various collective states, allowing us to describe processes such as activation, association, and deactivation of these states. |
| title | Statistical Field Theory and Neural Structures Dynamics IV: Field-Theoretic Formalism for Interacting Collective States |
| topic | Biological Physics Statistical Mechanics High Energy Physics - Theory Neurons and Cognition |
| url | https://arxiv.org/abs/2311.18417 |