ABSTRACT
Calcium imaging is a key method to record patterns of neuronal activity across populations of identified neurons. Inference of temporal patterns of action potentials (‘spikes’) from calcium signals is, however, challenging and often limited by the scarcity of ground truth data containing simultaneous measurements of action potentials and calcium signals. To overcome this problem, we compiled a large and diverse ground truth database from publicly available and newly performed recordings. This database covers various types of calcium indicators, cell types, and signal-to-noise ratios and comprises a total of >35 hours from 298 neurons. We then developed a novel algorithm for spike inference (CASCADE) that is based on supervised deep networks, takes advantage of the ground truth database, infers absolute spike rates, and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground truth data to match the respective sampling rate and noise level. As a consequence, no parameters need to be adjusted by the user. To facilitate routine application of CASCADE we developed systematic performance assessments for unseen data, we openly release all resources, and we provide a user-friendly cloud-based implementation.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵† Contributed equally
Addition of new datasets with interneuron data; completely revised comparison with other algorithms (new Fig. 4); comparison with artificial ground truth simulated with NAOMi (see Fig. 3); and various smaller additions: new Fig. S15 to analyze the performance of CASCADE as a function of ground truth dataset size, and Fig. S19 to analyze the performance of CASCADE and other algorithms as a function of temporal precision of predictions.
