神经解码的深度学习方法：从CNN到LSTM，从尖峰到fMRI
Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI
摘要：
直接从神经信号解码行为、感知或认知状态在脑机接口研究中有应用，也对系统神经科学有影响。在过去的十年中，深度学习已经成为从语音识别到图像分割的许多机器学习任务中最先进的方法。深度网络在其他领域的成功引发了神经科学应用的新浪潮。在这篇文章中，我们回顾了神经解码的深度学习方法。我们描述的架构用于提取有用的功能，从神经记录模式，从尖峰到EEG。此外，我们还将探讨如何利用深度学习来预测常见的输出，包括运动、语音和视觉，重点是如何将预先训练的深度网络作为声学语音或图像等复杂解码目标的先验知识。深度学习已被证明是一种有用的工具，可以在各种任务中提高神经解码的准确性和灵活性，我们指出了未来科学发展的领域。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
