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arXiv Machine Learning By Haowei Xu, Wanyi Fu, Hongbin Han, Zhaoheng Xie

NeuronSifter: Intervention Planning in CNS Microenvironments

Read the original on arXiv Machine Learning →

NeuronSifter is a framework for planning interventions in central nervous system microenvironments by converting treatment regimens into state‑conditional target‑occupancy fields and propagating them through microenvironment dynamics. It selects measurements based on their expected reduction in intervention loss, integrating typed outcomes into a unified posterior. In synthetic Alzheimer’s disease scenarios, occupancy conditioning improves trajectory probability scores and intervention ordering accuracy, and decision‑directed acquisition reduces terminal risk compared to a Bayesian experimental design planner.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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