Ubiquity of Uncertainty in Neuron Systems
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911067684208640 |
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| author | Le, Brandon B. Lamb, Bennett Benfer, Luke Sambangi, Sriharsha Vismith, Nisal Geemal Jagarapu, Akshaj |
| author_facet | Le, Brandon B. Lamb, Bennett Benfer, Luke Sambangi, Sriharsha Vismith, Nisal Geemal Jagarapu, Akshaj |
| contents | We demonstrate that final-state uncertainty is ubiquitous in multistable systems of coupled neuronal maps, meaning that predicting whether one such system will eventually be chaotic or nonchaotic is often nearly impossible. We propose a "chance synchronization" mechanism that governs the emergence of unpredictability in neuron systems and support it by using basin classification, uncertainty exponent, and basin entropy techniques to analyze five simple discrete-time systems, each consisting of a different neuron model. Our results illustrate that uncertainty in neuron systems is not just a product of noise or high-dimensional complexity; it is also a fundamental property of low-dimensional, deterministic models, which has profound implications for understanding brain function, modeling cognition, and interpreting unpredictability in general multistable systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15702 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ubiquity of Uncertainty in Neuron Systems Le, Brandon B. Lamb, Bennett Benfer, Luke Sambangi, Sriharsha Vismith, Nisal Geemal Jagarapu, Akshaj Neurons and Cognition Dynamical Systems Chaotic Dynamics Biological Physics We demonstrate that final-state uncertainty is ubiquitous in multistable systems of coupled neuronal maps, meaning that predicting whether one such system will eventually be chaotic or nonchaotic is often nearly impossible. We propose a "chance synchronization" mechanism that governs the emergence of unpredictability in neuron systems and support it by using basin classification, uncertainty exponent, and basin entropy techniques to analyze five simple discrete-time systems, each consisting of a different neuron model. Our results illustrate that uncertainty in neuron systems is not just a product of noise or high-dimensional complexity; it is also a fundamental property of low-dimensional, deterministic models, which has profound implications for understanding brain function, modeling cognition, and interpreting unpredictability in general multistable systems. |
| title | Ubiquity of Uncertainty in Neuron Systems |
| topic | Neurons and Cognition Dynamical Systems Chaotic Dynamics Biological Physics |
| url | https://arxiv.org/abs/2507.15702 |