Signal-to-signal neural networks for improved spike estimation from calcium imaging data.

ABSTRACT: Spiking information of individual neurons is essential for functional and behavioral analysis in neuroscience research. Calcium imaging techniques are generally employed to obtain activities of neuronal populations. However, these techniques result in slowly-varying fluorescence signals with low temporal resolution. Estimating the temporal positions of the neuronal action potentials from these signals is a challenging problem. In the literature, several generative model-based and data-driven algorithms have been studied with varied levels of success. This article proposes a neural network-based signal-to-signal conversion approach, where it takes as input raw-fluorescence signal and learns to estimate the spike information in an end-to-end fashion. Theoretically, the proposed approach form

SUBMITTER: Sebastian J 

PROVIDER: S-EPMC7951974 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature