{"ID":2892314,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.15702","arxiv_id":"2507.15702","title":"Ubiquity of Uncertainty in Neuron Systems","abstract":"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.","short_abstract":"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 i...","url_abs":"https://arxiv.org/abs/2507.15702","url_pdf":"https://arxiv.org/pdf/2507.15702v1","authors":"[\"Brandon B. Le\",\"Bennett Lamb\",\"Luke Benfer\",\"Sriharsha Sambangi\",\"Nisal Geemal Vismith\",\"Akshaj Jagarapu\"]","published":"2025-07-21T15:11:23Z","proceeding":"q-bio.NC","tasks":"[\"q-bio.NC\",\"math.DS\",\"nlin.CD\",\"physics.bio-ph\"]","methods":"[]","has_code":false}