Direct estimates of irreversibility from time series
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910765008551936 |
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| author | GrandPre, Trevor Teza, Gianluca Bialek, William |
| author_facet | GrandPre, Trevor Teza, Gianluca Bialek, William |
| contents | The arrow of time can be quantified through the Kullback-Leibler divergence ($D_{KL}$) between the distributions of forward and reverse trajectories in a system. Many approaches to estimate this rely on specific models, but the use of incorrect models can introduce uncontrolled errors. Here, we describe a model-free method that uses trajectory data directly to estimate the evidence for irreversibility over finite windows of time. To do this we build on previous work to identify and correct for errors that arise from limited sample size. Importantly, our approach accurately recovers $D_{KL} = 0$ in systems that adhere to detailed balance, and the correct nonzero $D_{KL}$ for data generated by well understood models of nonequilibrium systems. We apply our method to trajectories of neural activity in the retina as it responds to naturalistic inputs, and find evidence of irreversibility in single neurons, emphasizing the non-Markovian character of these data. These results open new avenues for investigating how the brain represents the arrow of time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_19772 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Direct estimates of irreversibility from time series GrandPre, Trevor Teza, Gianluca Bialek, William Statistical Mechanics Biological Physics Neurons and Cognition Applications The arrow of time can be quantified through the Kullback-Leibler divergence ($D_{KL}$) between the distributions of forward and reverse trajectories in a system. Many approaches to estimate this rely on specific models, but the use of incorrect models can introduce uncontrolled errors. Here, we describe a model-free method that uses trajectory data directly to estimate the evidence for irreversibility over finite windows of time. To do this we build on previous work to identify and correct for errors that arise from limited sample size. Importantly, our approach accurately recovers $D_{KL} = 0$ in systems that adhere to detailed balance, and the correct nonzero $D_{KL}$ for data generated by well understood models of nonequilibrium systems. We apply our method to trajectories of neural activity in the retina as it responds to naturalistic inputs, and find evidence of irreversibility in single neurons, emphasizing the non-Markovian character of these data. These results open new avenues for investigating how the brain represents the arrow of time. |
| title | Direct estimates of irreversibility from time series |
| topic | Statistical Mechanics Biological Physics Neurons and Cognition Applications |
| url | https://arxiv.org/abs/2412.19772 |