A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging.

ABSTRACT: Inference of action potentials ('spikes') from neuronal calcium signals is complicated by the scarcity of simultaneous measurements of action potentials and calcium signals ('ground truth'). In this study, we compiled a large, diverse ground truth database from publicly available and newly performed recordings in zebrafish and mice covering a broad range of calcium indicators, cell types and signal-to-noise ratios, comprising a total of more than 35 recording hours from 298 neurons. We developed an algorithm for spike inference (termed 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 resampl

SUBMITTER: Rupprecht P 

PROVIDER: S-EPMC7611618 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature