GDspike: An accurate spike estimation algorithm from noisy calcium fluorescence signals

Abstract

Accurate estimation of spike train from calcium (Ca <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2+</sup> ) fluorescence signals is challenging owing to significant fluctuations of fluorescence level. This paper proposes a non-model-based approach for spike train inference using group delay (GD) analysis. It primarily exploits the property that change in Ca <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2+</sup> fluorescence corresponding to a spike has a notable onset location followed by a decaying transient. The proposed algorithm, GDspike, is compared with state-of-the-art systems on five datasets. F-measure is best for GDspike (41%) followed by STM (40%), MLspike (39%), and Vogelstein (35%). While existing methods are inspired by the physiology of neuronal responses, the proposed approach is inspired by GD-based high-resolution processing of the Ca <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2+</sup> fluorescence signal. GDspike is a fast and unsupervised algorithm. It is found to be unaffected when tested with five different GCaMP indicators and scanning rate varying from 15Hz to 60Hz.