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Decoding behavior, perception, or cognitive state directly from neuralsignals has applications in brain-computer interface research as well asimplications for systems neuroscience. In the last decade, deep learning hasbecome the state-of-the-art method in many machine learning tasks ranging fromspeech recognition to image segmentation. The success of deep networks in otherdomains has led to a new wave of applications in neuroscience. In this article,we review deep learning approaches to neural decoding. We describe thearchitectures used for extracting useful features from neural recordingmodalities ranging from spikes to EEG. Furthermore, we explore how deeplearning has been leveraged to predict common outputs including movement,speech, and vision, with a focus on how pretrained deep networks can beincorporated as priors for complex decoding targets like acoustic speech orimages. Deep learning has been shown to be a useful tool for improving theaccuracy and flexibility of neural decoding across a wide range of tasks, andwe point out areas for future scientific development.

Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI