Magneto- and electroencephalography (M/EEG) are widespread techniques to measure neural activity in vivo at a high temporal resolution but relatively low spatial resolution. Locating the sources underlying the M/EEG poses an inverse problem, which is itself ill-posed. In recent years, a new class of source imaging methods was developed based on artificial neural networks. We present a long-short term memory (LSTM) network to solve the M/EEG inverse problem. It integrates several aspects

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... l for qualitative inverse solutions: Low computational cost, exploitation of both spatial and temporal information, input flexibility and robustness to noise. Using simulation data, the LSTM shows higher accuracy on multiple metrics and for varying numbers of neural sources, compared to classical inverse solutions but also compared to our alternative architectures without integration of temporal information. It successfully integrates the temporal context given by sequential data, which is particularly useful with noisy data. Real data of a median nerve stimulation paradigm was used to show that the LSTM predicts plausible sources that are in concordance with classical inverse solutions. The performance of the LSTM regarding its robustness to noise renders it a promising and easy-to-apply inverse solution to be considered in future source localization studies and for clinical applications.