Berens, P;
Freeman, J;
Deneux, T;
Chenkov, N;
McColgan, T;
Speiser, A;
Macke, JH;
... Bethge, M; + view all
(2018)
Community-based benchmarking improves spike rate inference from two-photon calcium imaging data.
PLoS Computational Biology
, 14
(5)
, Article e1006157. 10.1371/journal.pcbi.1006157.
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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.
| Type: | Article |
|---|---|
| Title: | Community-based benchmarking improves spike rate inference from two-photon calcium imaging data |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1371/journal.pcbi.1006157 |
| Publisher version: | https://doi.org/10.1371/journal.pcbi.1006157 |
| Language: | English |
| Additional information: | © 2018 Berens et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Algorithms, Action potentials, Calcium signaling, Machine learning algorithms, Neurons, Calcium imaging, Ranking algorithms, Neural networks |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Department of Neuromuscular Diseases |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10052882 |
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