Spiking information of individual neurons is essential for functional and behavioural analysis in neuroscience. During electrophysiological experiments in animals, calcium imaging techniques are employed to obtain activities of individual neurons and neuronal populations and they result in slowly-varying fluorescence signals with poor temporal resolution. Estimating the temporal positions of action potentials from these signals is a challenging problem. In the literature, a number of generative

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... model based and data-driven algorithms have been studied with limited success. In this article, we propose a neural network based signal-to-signal (S2S) conversion approach, where the neural network takes as input raw-fluorescence signal and learns to predict spike information signal in an end-to-end manner. Theoretically, the proposed approach formulates the problem of spike estimation from the fluorescence signal as a single channel source separation problem with unknown mixing conditions. Through experimental studies on spikefinder bench-marking dataset, we show that the proposed S2S conversion approach outperforms state-of-the-art-methods. We show that the resulting system: (a) has low complexity with respect to existing approaches and is reproducible; (b) is layer-wise interpretable; and (c) has the capability to generalise across different calcium indicators.