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State and Parameter Estimation for a Neural Model of Local Field Potentials
Daniele Avitabile, Gabriel J. Lord, Khadija Meddouni
https://arxiv.org/abs/2512.07842 https://arxiv.org/pdf/2512.07842 https://arxiv.org/html/2512.07842
arXiv:2512.07842v1 Announce Type: new
Abstract: The study of cortical dynamics during different states such as decision making, sleep and movement, is an important topic in Neuroscience. Modelling efforts aim to relate the neural rhythms present in cortical recordings to the underlying dynamics responsible for their emergence. We present an effort to characterize the neural activity from the cortex of a mouse during natural sleep, captured through local field potential measurements. Our approach relies on using a discretized Wilson--Cowan Amari neural field model for neural activity, along with a data assimilation method that allows the Bayesian joint estimation of the state and parameters. We demonstrate the feasibility of our approach on synthetic measurements before applying it to a dataset available in literature. Our findings suggest the potential of our approach to characterize the stimulus received by the cortex from other brain regions, while simultaneously inferring a state that aligns with the observed signal.
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📊 Statistical robustness - run tests N times with pass rate thresholds (e.g., 80% of 10 runs must pass) to catch flaky prompts

"People should not "blindly trust" everything AI tools tell them, the boss of Google's parent company Alphabet told the BBC.
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Ensemble-Based Data Assimilation for Material Model Characterization in High-Velocity Impact
Rong Jin, Guangyao Wang, Xingsheng Sun
https://arxiv.org/abs/2510.09703 https://

Ensemble-Based Data Assimilation for Material Model Characterization in High-Velocity Impact
High-fidelity simulations are essential for understanding and predicting the behavior of materials under high-velocity impact (HVI) in both fundamental research and practical applications. However, their accuracy relies on material models and parameters that are traditionally obtained through manual fitting to multiple time- and labor-intensive experiments. This study presents an ensemble-based data assimilation (DA) framework to automatically and simultaneously calibrate plasticity, fracture, …

Nonlinear Heisenberg Limit via Uncertainty Principle in Quantum Metrology
Binke Xia, Jingzheng Huang, Yuxiang Yang, Guihua Zeng
https://arxiv.org/abs/2510.09216 https://

Nonlinear Heisenberg Limit via Uncertainty Principle in Quantum Metrology
The Heisenberg limit is acknowledged as the ultimate precision limit in quantum metrology, traditionally implying that root mean square errors of parameter estimation decrease linearly with the time T of evolution and the number N of quantum gates or probes. However, this conventional perspective fails to interpret recent studies of "super-Heisenberg" scaling, where precision improves faster than linearly with T and N. In this work, we revisit the Heisenberg scaling by leveraging the position-m…