论文标题
转移学习以解码大脑状态，以反映认知任务之间的关系
Transfer learning to decode brain states reflecting the relationship between cognitive tasks
论文作者
论文摘要
转移学习通过利用特定源任务的数据来提高目标任务的性能：源和目标任务之间的关系越接近，通过转移学习的绩效越大。在神经科学中，认知任务之间的关系通常由激活的大脑区域或神经表示的相似性表示。但是，没有研究将转移学习和神经科学联系起来，以揭示认知任务之间的关系。在这项研究中，我们提出了一个转移学习框架，以反映认知任务之间的关系，并比较通过转移学习和大脑区域（例如Neurosynth）反映的任务关系。我们的转移学习结果创建了认知任务，以反映认知任务之间的关系，这与来自神经合成的任务关系非常一致。如果源和目标认知任务激活相似的大脑区域，则传递学习在任务解码方面的性能更好。我们的研究发现了多个认知任务的关系，并为基于小样本数据的神经解码转移学习的源任务选择提供了指导。
Transfer learning improves the performance of the target task by leveraging the data of a specific source task: the closer the relationship between the source and the target tasks, the greater the performance improvement by transfer learning. In neuroscience, the relationship between cognitive tasks is usually represented by similarity of activated brain regions or neural representation. However, no study has linked transfer learning and neuroscience to reveal the relationship between cognitive tasks. In this study, we propose a transfer learning framework to reflect the relationship between cognitive tasks, and compare the task relations reflected by transfer learning and by the overlaps of brain regions (e.g., neurosynth). Our results of transfer learning create cognitive taskonomy to reflect the relationship between cognitive tasks which is well in line with the task relations derived from neurosynth. Transfer learning performs better in task decoding with fMRI data if the source and target cognitive tasks activate similar brain regions. Our study uncovers the relationship of multiple cognitive tasks and provides guidance for source task selection in transfer learning for neural decoding based on small-sample data.
