Abstract
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 populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, 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 rate 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.
Highlights
Two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations [1, 2]
We used five benchmark data sets consisting in total of 92 recordings from 73 neurons, acquired in the primary visual cortex and the retina of mice
Calcium imaging had been performed simultaneously with electrophysiological recordings allowing to evaluate the performance of spike rate inference algorithms on ground truth data [15]
Summary
Two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations [1, 2]. Many of them assume a forward generative model of the calcium signal and attempt to invert it to infer spike rates Examples of this approach include deconvolution techniques [9, 10], template-matching [4, 11] and approximate Bayesian inference [6, 12, 13]. Such forward models incorporate a priori assumptions about how the measured signal is generated, e.g. about the shape of the calcium fluorescence signal induced by a single spike and the statistics of the noise. Comparatively few groups have attempted to solve the problem through supervised learning [14, 15], where a machine learning algorithm is trained to infer the spike rate from calcium signal using simultaneously recorded spike and calcium data for training
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