Community-based benchmarking improves spike inference from two-photon calcium imaging data
Community-based benchmarking improves spike inference from two-photon calcium imaging data
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DOI：
10.1101/177956
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发表时间：
2017-08
期刊：
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通讯作者：
Philipp Berens;Jeremy Freeman;Thomas Deneux;Nicolay Chenkov;Thomas McColgan;Artur Speiser;J. Macke;Srinivas C. Turaga;Patrick J. Mineault;Peter Rupprecht;S. Gerhard;R. Friedrich;Johannes Friedrich;L. Paninski;Marius Pachitariu;K. Harris;Ben Bolte;Timothy A. Machado;D. Ringach;Jasmine Stone;L. Rogerson;N. Sofroniew;Jacob Reimer;E. Froudarakis;Thomas Euler;M. Rosón;Lucas Theis;A. Tolias;M. Bethge
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文献类型：
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作者：
Philipp Berens;Jeremy Freeman;Thomas Deneux;Nicolay Chenkov;Thomas McColgan;Artur Speiser;J. Macke;Srinivas C. Turaga;Patrick J. Mineault;Peter Rupprecht;S. Gerhard;R. Friedrich;Johannes Friedrich;L. Paninski;Marius Pachitariu;K. Harris;Ben Bolte;Timothy A. Machado;D. Ringach;Jasmine Stone;L. Rogerson;N. Sofroniew;Jacob Reimer;E. Froudarakis;Thomas Euler;M. Rosón;Lucas Theis;A. Tolias;M. Bethge
近年来，双光子钙成像已成为探测神经回路功能和研究神经元群体计算的标准工具1、2。然而，由于荧光钙指示剂的动态相对较慢，所获取的信号仅是神经活动的间接测量3。过去已经提出了根据噪声钙测量估计尖峰序列的不同算法4-8，但性能能提高多少仍然是一个悬而未决的问题。在这里，我们报告了Spikefinder挑战赛的结果，该挑战赛的发起是为了通过众包促进新的Spike推理算法的开发。我们提出了十种提交的算法，与之前评估的方法相比，这些算法显示出改进的性能。有趣的是，性能最好的算法基于从深度神经网络到生成模型的广泛原理，但提供了神经活动的高度相关估计。比赛表明基准挑战可以推动神经科学的算法发展。
In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations1, 2. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators3. Different algorithms for estimating spike trains from noisy calcium measurements have been proposed in the past4‒8, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.