Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI
This is an incremental review article summarizing existing methods for brain-computer interfaces and neuroscience applications.
This paper reviews deep learning approaches for neural decoding, tackling the problem of decoding behavior, perception, or cognitive state from neural signals, and finds that deep learning improves accuracy and flexibility across tasks such as movement, speech, and vision prediction.
Decoding behavior, perception, or cognitive state directly from neural signals has applications in brain-computer interface research as well as implications for systems neuroscience. In the last decade, deep learning has become the state-of-the-art method in many machine learning tasks ranging from speech recognition to image segmentation. The success of deep networks in other domains has led to a new wave of applications in neuroscience. In this article, we review deep learning approaches to neural decoding. We describe the architectures used for extracting useful features from neural recording modalities ranging from spikes to EEG. Furthermore, we explore how deep learning has been leveraged to predict common outputs including movement, speech, and vision, with a focus on how pretrained deep networks can be incorporated as priors for complex decoding targets like acoustic speech or images. Deep learning has been shown to be a useful tool for improving the accuracy and flexibility of neural decoding across a wide range of tasks, and we point out areas for future scientific development.