Adaptation to time-varying environments in a reaction-diffusion model
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918225270276096 |
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| author | Rivoire, Olivier Bunin, Guy |
| author_facet | Rivoire, Olivier Bunin, Guy |
| contents | We present a spatially-extended system of chemical reactions exhibiting adaptation to time-dependent influxes of reactants. Here adaptation is defined as improved reproductive success, namely the ability of one of the many locally stable states available to the system to expand in space at the expense of other states. We find that adaptation can arise simply by environmental exposure to sequences of varying influxes. This adaptation is specific to the temporal sequence yet flexible enough to generalize to related sequences. It is enhanced through repeated exposure to the same environmental sequence, representing a form of learning, and through spatial interactions, enabling natural selection to act and representing a form of collective learning. Finally, adaptation benefits from a nearby adapted state, representing a form of teacher-guided learning. By combining environmental drives and reproduction within a stochastic reaction-diffusion dynamics framework, our model lays a foundation for a theory of adaptation grounded in physical principles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_01007 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptation to time-varying environments in a reaction-diffusion model Rivoire, Olivier Bunin, Guy Statistical Mechanics Populations and Evolution We present a spatially-extended system of chemical reactions exhibiting adaptation to time-dependent influxes of reactants. Here adaptation is defined as improved reproductive success, namely the ability of one of the many locally stable states available to the system to expand in space at the expense of other states. We find that adaptation can arise simply by environmental exposure to sequences of varying influxes. This adaptation is specific to the temporal sequence yet flexible enough to generalize to related sequences. It is enhanced through repeated exposure to the same environmental sequence, representing a form of learning, and through spatial interactions, enabling natural selection to act and representing a form of collective learning. Finally, adaptation benefits from a nearby adapted state, representing a form of teacher-guided learning. By combining environmental drives and reproduction within a stochastic reaction-diffusion dynamics framework, our model lays a foundation for a theory of adaptation grounded in physical principles. |
| title | Adaptation to time-varying environments in a reaction-diffusion model |
| topic | Statistical Mechanics Populations and Evolution |
| url | https://arxiv.org/abs/2512.01007 |