A finite rate of innovation algorithm for fast and accurate spike detection from two-photon calcium imaging

Journal of Neural Engineering
Jon OñativiaPier Luigi Dragotti

Abstract

Inferring the times of sequences of action potentials (APs) (spike trains) from neurophysiological data is a key problem in computational neuroscience. The detection of APs from two-photon imaging of calcium signals offers certain advantages over traditional electrophysiological approaches, as up to thousands of spatially and immunohistochemically defined neurons can be recorded simultaneously. However, due to noise, dye buffering and the limited sampling rates in common microscopy configurations, accurate detection of APs from calcium time series has proved to be a difficult problem. Here we introduce a novel approach to the problem making use of finite rate of innovation (FRI) theory (Vetterli et al 2002 IEEE Trans. SIGNAL PROCESS: 50 1417-28). For calcium transients well fit by a single exponential, the problem is reduced to reconstructing a stream of decaying exponentials. Signals made of a combination of exponentially decaying functions with different onset times are a subclass of FRI signals, for which much theory has recently been developed by the signal processing community. Main results. We demonstrate for the first time the use of FRI theory to retrieve the timing of APs from calcium transient time series. The final a...Continue Reading

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Dec 19, 2013·Computational Intelligence and Neuroscience·Zhe Chen
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Related Concepts

Nerve Impulses
Metazoa
Cerebellum
Image Interpretation, Computer-Assisted
Neurons
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Reproducibility of Results
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