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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Editorial: Machine learning for computational neural modeling and data analyses

Youhui Zhang1, Yiran Chen2, Yuanyuan Mi3

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Summary

No abstract available in PubMed .

Keywords:
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