论文标题
在自然和人造代理商中持续的任务学习
Continual task learning in natural and artificial agents
论文作者
论文摘要
人类和其他动物如何学习新任务？大脑记录研究的一波研究调查了在任务学习过程中神经表示如何变化，重点是如何以最小化相互干扰的方式获取和编码任务。我们回顾了最近探讨了新皮层中神经任务表示的几何形状和维度的最新工作，以及利用这些发现以了解大脑如何在任务之间分配知识的计算模型。我们讨论了来自机器学习的想法，包括结合受监督和无监督的学习的思想如何帮助神经科学家了解如何在生物学大脑中学习和编码自然任务。
How do humans and other animals learn new tasks? A wave of brain recording studies has investigated how neural representations change during task learning, with a focus on how tasks can be acquired and coded in ways that minimise mutual interference. We review recent work that has explored the geometry and dimensionality of neural task representations in neocortex, and computational models that have exploited these findings to understand how the brain may partition knowledge between tasks. We discuss how ideas from machine learning, including those that combine supervised and unsupervised learning, are helping neuroscientists understand how natural tasks are learned and coded in biological brains.
