AI 中文总结
研究针对全光双光子全息光遗传学中详尽连接性映射难题，提出OPhELIA贝叶斯框架，结合多种方法，在模拟和斑马鱼实验中，该框架用较少试验实现高效近似或恢复详尽连接组，是因果连接组学的样本高效框架。
AI 中文摘要
全光双光子全息光遗传学能够通过在成像群体活动时刺激特定神经元或神经元群来进行因果回路映射。然而，由于组合复杂性、组织加热、光损伤和实验时间等因素，详尽的连接性映射在实验上仍然难以实现。我们提出了OPhELIA（用于迭代活动图的最优光刺激选择），这是一个在有限试验预算下选择信息性扰动的贝叶斯框架。OPhELIA将贝塔-伯努利连接性推断与基于模糊性的采集启发式方法以及从预刺激神经活动中学习到的先验知识相结合，增强了主动学习和压缩感知。在独立模拟和体内斑马鱼幼体视觉运动实验中，结合主动学习的OPhELIA提高了对详尽功能连接组的试验效率近似。在组合体内实验中，结合压缩感知的OPhELIA仅使用5%的试验就能最接近地恢复详尽连接组。这些结果将OPhELIA确立为因果连接组学的样本高效框架。
英文摘要
All-optical two-photon holographic optogenetics enables causal circuit mapping by stimulating defined neurons or ensembles while imaging population activity. Yet exhaustive connectivity mapping remains experimentally prohibitive because of combinatorial complexity, tissue heating, photodamage, and experimental time. We present OPhELIA (Optimal Photostimulation sElection for Iterative Activity maps), a Bayesian framework for selecting informative perturbations under limited trial budgets. OPhELIA combines Beta-Bernoulli connectivity inference with an ambiguity-based acquisition heuristic and learned priors derived from pre-stimulation neural activity, augmenting active learning and compressed sensing. In standalone simulations and in vivo larval zebrafish visuomotor experiments, OPhELIA with active learning improves trial-efficient approximation of exhaustive functional connectomes. In combinatorial in vivo experiments, OPhELIA with compressed sensing most closely recovers an exhaustive connectome using only 5% of trials. These results establish OPhELIA as a sample-efficient framework for causal connectomics.
Comments17 pages, 6 figures
