Exploring the origins of switching dynamics in a multifunctional reservoir computer
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909299139149824 |
|---|---|
| author | Flynn, Andrew Amann, Andreas |
| author_facet | Flynn, Andrew Amann, Andreas |
| contents | The concept of multifunctionality has enabled reservoir computers (RCs), a type of dynamical system that is typically realised as an artificial neural network, to reconstruct multiple attractors simultaneously using the same set of trained weights. However there are many additional phenomena that arise when training a RC to reconstruct more than one attractor. Previous studies have found that, in certain cases, if the RC fails to reconstruct a coexistence of attractors then it exhibits a form of metastability whereby, without any external input, the state of the RC switches between different modes of behaviour that resemble properties of the attractors it failed to reconstruct. In this paper we explore the origins of these switching dynamics in a paradigmatic setting via the `seeing double' problem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_15400 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring the origins of switching dynamics in a multifunctional reservoir computer Flynn, Andrew Amann, Andreas Dynamical Systems Machine Learning Neural and Evolutionary Computing The concept of multifunctionality has enabled reservoir computers (RCs), a type of dynamical system that is typically realised as an artificial neural network, to reconstruct multiple attractors simultaneously using the same set of trained weights. However there are many additional phenomena that arise when training a RC to reconstruct more than one attractor. Previous studies have found that, in certain cases, if the RC fails to reconstruct a coexistence of attractors then it exhibits a form of metastability whereby, without any external input, the state of the RC switches between different modes of behaviour that resemble properties of the attractors it failed to reconstruct. In this paper we explore the origins of these switching dynamics in a paradigmatic setting via the `seeing double' problem. |
| title | Exploring the origins of switching dynamics in a multifunctional reservoir computer |
| topic | Dynamical Systems Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2408.15400 |