Benchmarking Spike Rate Inference in Population Calcium Imaging.
Benchmarking Spike Rate Inference in Population Calcium Imaging.
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DOI：
10.1016/j.neuron.2016.04.014
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发表时间：
2016-05-04
期刊：
影响因子：
16.2
通讯作者：
Bethge M
中科院分区：
文献类型：
--
作者：
Theis L;Berens P;Froudarakis E;Reimer J;Román Rosón M;Baden T;Euler T;Tolias AS;Bethge M
钙成像中的一个基本挑战是从测量的噪声荧光迹线推断神经元的尖峰速率。我们使用不同的钙指标（OGB-1和GCaMP 6）在从不同的神经组织（V1和视网膜）记录的大型基准数据集（>100.000个尖峰）上系统地评估了不同的尖峰推断算法。此外，我们引入了一个新的算法，基于监督学习灵活的概率模型，并发现它比其他已发表的技术表现更好。重要的是，即使应用于没有同时记录数据的全新数据集，它也优于其他算法。未来在新的实验条件下获得的数据可用于进一步提高该模型的锋电位预测精度和泛化性能。最后，我们表明，比较人工数据上的算法是没有信息的性能真实的数据，表明基准不同的方法与真实世界的数据集可能会大大促进未来的算法在神经科学的发展。
A fundamental challenge in calcium imaging has been to infer spike rates of neurons from the measured noisy fluorescence traces. We systematically evaluate different spike inference algorithms on a large benchmark dataset (>100.000 spikes) recorded from varying neural tissue (V1 and retina) using different calcium indicators (OGB-1 and GCaMP6). In addition, we introduce a new algorithm based on supervised learning in flexible probabilistic models and find that it performs better than other published techniques. Importantly, it outperforms other algorithms even when applied to entirely new datasets for which no simultaneously recorded data is available. Future data acquired in new experimental conditions can be used to further improve the spike prediction accuracy and generalization performance of the model. Finally, we show that comparing algorithms on artificial data is not informative about performance on real data, suggesting that benchmarking different methods with real-world datasets may greatly facilitate future algorithmic developments in neuroscience.