Benchmarking Spike Rate Inference in Population Calcium Imaging

Abstract

Summary 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.

In vivo imaging of neural activity

“…Novel fluorophores 8,63,97,98 and data analysis algorithms 65,67,99 are critical for in vivo fluorescence imaging, although they are not reviewed here. In addition, although focused on calcium imaging, our discussion also applies to the next generation of voltage imaging probes.…”

Improving data quality in neuronal population recordings

“…Calcium signals are correlated with neuronal spiking, but are an indirect reflection of it, and biophysical variations make the precise relationship between calcium signals and spiking variable 87 . Calcium reporters can themselves limit the precision of spike inference: although synthetic calcium dyes can be used in a linear regime, they still exhibit nonlinear features including saturation; and genetically encoded calcium indicator (GECI) proteins are highly nonlinear due to cooperative Ca 2+ binding 88 .…”

“…For example, GCaMP6s imaging can provide single AP detection with nearly 100% detection of all spikes when imaged at a high frame rate over a small field-of-view (30 μm × 30 μm at 60 frames/s) 91 . However, larger field-of-view, population-level imaging (265 μm × 265 μm at 59.1 frames/s) yields spike rate estimates that correlate with the true spike rate at an average level of ~ 0.5 (Pearson's R , using a 50 ms spike rate binning window) 87 . Quality is also affected by indicator properties and labeling intensity 88, 94 .…”

“…With GCaMP6f, for example, spike pairs can exhibit fluorescence transients whose size depends on inter-spike interval (personal communication from D. DiGregorio and S. Wang). As with segmentation, supervised learning methods using systematic ground truth data are a promising alternative to unsupervised deconvolution 87 . With present technology, estimates of spike times from calcium imaging should always be treated as approximations, though this uncertainty can be propagated through stages of analysis (Fig.…”

Axial localization with modulated-illumination extended-depth-of-field microscopy

“…8(a)-(b)). However, the overlap intensity alone would not be sufficient to enable the association of a particular transient to a particular neuron, without recourse to statistical correlations over non-overlapping regions ( [25,26]). Using our axial localization technique, analysis of the axial positioning data (Fig.…”