Inferring parameters of pyramidal neuron excitability in mouse models of Alzheimer's disease using biophysical modeling and deep learning
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Publication:6124370
DOI10.1007/S11538-024-01273-5OpenAlexW4393145769MaRDI QIDQ6124370
Kyle C. A. Wedgwood, Viatcheslav Gurev, Casey O. Diekman, Soheil Saghafi, Francesco Tamagnini, James Kozloski, Timothy Rumbell
Publication date: 27 March 2024
Published in: Bulletin of Mathematical Biology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11538-024-01273-5
Neural networks for/in biological studies, artificial life and related topics (92B20) Pathology, pathophysiology (92C32)
Cites Work
- Unnamed Item
- Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces
- A methodology for performing global uncertainty and sensitivity analysis in systems biology
- Solving inverse problems in stochastic models using deep neural networks and adversarial training
- Parameter estimation with maximal updated densities
- Inference for Deterministic Simulation Models: The Bayesian Melding Approach
- Combining Push-Forward Measures and Bayes' Rule to Construct Consistent Solutions to Stochastic Inverse Problems
- The Principles of Deep Learning Theory
- The frontier of simulation-based inference
- Considering discrepancy when calibrating a mechanistic electrophysiology model
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