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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 functional magnetic resonance imaging. 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.<\/jats:p>","DOI":"10.1093\/bib\/bbaa355","type":"journal-article","created":{"date-parts":[[2020,11,19]],"date-time":"2020-11-19T04:59:30Z","timestamp":1605761970000},"page":"1577-1591","source":"Crossref","is-referenced-by-count":65,"title":["Deep learning approaches for neural decoding across architectures and recording modalities"],"prefix":"10.1093","volume":"22","author":[{"given":"Jesse A","family":"Livezey","sequence":"first","affiliation":[{"name":"Neural Systems and Data Science Laboratory at the Lawrence Berkeley National Laboratory. He obtained his PhD in Physics from the University of California, Berkeley"}]},{"given":"Joshua I","family":"Glaser","sequence":"additional","affiliation":[{"name":"Center for Theoretical Neuroscience and Department of Statistics at Columbia University. He obtained his PhD in Neuroscience from Northwestern University"}]}],"member":"286","published-online":{"date-parts":[[2020,12,29]]},"reference":[{"issue":"6","key":"2021122901304151200_ref1","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1016\/S1388-2457(02)00057-3","article-title":"Brain\u2013computer interfaces for communication and control","volume":"113","author":"Wolpaw","year":"2002","journal-title":"Clin Neurophysiol"},{"key":"2021122901304151200_ref2","article-title":"A survey on deep learning based brain computer interface: recent advances and new frontiers","author":"Zhang","year":"2019","journal-title":"arXiv preprint arXiv:190504149"},{"issue":"13","key":"2021122901304151200_ref3","doi-asserted-by":"crossref","first-page":"3615","DOI":"10.1523\/JNEUROSCI.3468-05.2006","article-title":"Movement intention is better predicted than attention in the posterior parietal cortex","volume":"26","author":"Quiroga","year":"2006","journal-title":"J 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