Benchmarking spike rate inference in population calcium imaging

Theis, Lucas, Berens, Philipp, Froudarakis, Emmanouil, Reimer, Jacob, Román Rosón, Miroslav, Baden, Tom, Euler, Thomas, Tolias, Andreas S and Bethge, Matthias (2016) Benchmarking spike rate inference in population calcium imaging. Neuron, 90 (3). pp. 471-482. ISSN 0896-6273

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Abstract

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.

Item Type: Article
Schools and Departments: School of Life Sciences > Neuroscience
Depositing User: Thomas Baden
Date Deposited: 20 Jun 2017 15:40
Last Modified: 05 Jul 2017 11:46
URI: http://sro.sussex.ac.uk/id/eprint/68768

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