TY - JOUR T1 - Community-based benchmarking improves spike inference from two-photon calcium imaging data JF - bioRxiv DO - 10.1101/177956 SP - 177956 AU - Philipp Berens AU - Jeremy Freeman AU - Thomas Deneux AU - Nicolay Chenkov AU - Thomas McColgan AU - Artur Speiser AU - Jakob H. Macke AU - Srinivas C. Turaga AU - Patrick Mineault AU - Peter Rupprecht AU - Stephan Gerhard AU - Rainer W. Friedrich AU - Johannes Friedrich AU - Liam Paninski AU - Marius Pachitariu AU - Kenneth D. Harris AU - Ben Bolte AU - Timothy A. Machado AU - Dario Ringach AU - Jasmine Stone AU - Nicolas J. Sofroniew AU - Jacob Reimer AU - Emmanoulis Froudarakis AU - Thomas Euler AU - Miroslav Román Rosón AU - Lucas Theis AU - Andreas S. Tolias AU - Matthias Bethge Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/08/19/177956.abstract N2 - 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. ER -