Task representations in neural networks trained to perform many cognitive tasks
Task representations in neural networks trained to perform many cognitive tasks
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DOI：
10.1038/s41593-018-0310-2
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发表时间：
2019-02-01
影响因子：
25
通讯作者：
Wang, Xiao-Jing
中科院分区：
文献类型：
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作者：
Yang, Guangyu Robert;Joglekar, Madhura R.;Wang, Xiao-Jing
大脑有能力灵活地执行许多任务，但在传统的实验和建模研究中，一次只针对一项任务，无法阐明其潜在机制。在这里，我们训练了单个网络模型来执行20个认知任务，这些任务取决于工作记忆、决策、分类和抑制控制。我们发现，经过训练，循环单元可以发展成功能上专门用于不同认知过程的集群，我们引入了一个简单而有效的措施来量化任务的单单元神经表征之间的关系。学习通常会产生任务表征的组合性，这是认知灵活性的一个关键特征，一项任务可以通过重组其他任务的指令来完成。最后，网络开发的混合任务选择性类似于记录前额叶神经元学习多个任务后，连续学习技术。这项工作提供了一个计算平台，研究许多认知任务的神经表征。
The brain has the ability to flexibly perform many tasks, but the underlying mechanism cannot be elucidated in traditional experimental and modeling studies designed for one task at a time. Here, we trained single network models to perform 20 cognitive tasks that depend on working memory, decision making, categorization, and inhibitory control. We found that after training, recurrent units can develop into clusters that are functionally specialized for different cognitive processes, and we introduce a simple yet effective measure to quantify relationships between single-unit neural representations of tasks. Learning often gives rise to compositionality of task representations, a critical feature for cognitive flexibility, whereby one task can be performed by recombining instructions for other tasks. Finally, networks developed mixed task selectivity similar to recorded prefrontal neurons after learning multiple tasks sequentially with a continual-learning technique. This work provides a computational platform to investigate neural representations of many cognitive tasks.