Working memory is essential: it serves to guide intelligent behavior of humans and nonhuman primates when task-relevant stimuli are no longer present to the senses. Moreover, complex tasks often require that multiple working memory representations can be flexibly and independently maintained, prioritized, and updated according to changing task demands. Thus far, neural network models of working memory have been unable to offer an integrative account of how such control mechanisms can be acquired in a biologically plausible manner. Here, we present WorkMATe, a neural network architecture that models cognitive control over working memory content and learns the appropriate control operations needed to solve complex working memory tasks. Key components of the model include a gated memory circuit that is controlled by internal actions, encoding sensory information through untrained connections, and a neural circuit that matches sensory inputs to memory content. The network is trained by means of a biologically plausible reinforcement learning rule that relies on attentional feedback and reward prediction errors to guide synaptic updates. We demonstrate that the model successfully acquires policies to solve classical working memory tasks, such as delayed recognition and delayed pro-saccade/anti-saccade tasks. In addition, the model solves much more complex tasks, including the hierarchical 12-AX task or the ABAB ordered recognition task, both of which demand an agent to independently store and updated multiple items separately in memory. Furthermore, the control strategies that the model acquires for these tasks subsequently generalize to new task contexts with novel stimuli, thus bringing symbolic production rule qualities to a neural network architecture. As such, WorkMATe provides a new solution for the neural implementation of flexible memory control.

中文翻译：

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通过选择性门控和注意标记灵活的工作记忆

工作记忆是必不可少的：当与任务相关的刺激不再存在于感官时，它可以指导人类和非人类灵长类动物的智能行为。此外，复杂的任务通常需要根据不断变化的任务需求灵活独立地维护、确定优先级和更新多个工作记忆表示。迄今为止，工作记忆的神经网络模型无法综合说明如何以生物学上合理的方式获得这种控制机制。在这里，我们展示了 WorkMATe，这是一种神经网络架构，可对工作记忆内容的认知控制进行建模，并学习解决复杂工作记忆任务所需的适当控制操作。该模型的关键组件包括一个由内部动作控制的门控记忆电路，通过未经训练的连接对感官信息进行编码，以及将感官输入与记忆内容相匹配的神经回路。该网络通过生物学上合理的强化学习规则进行训练，该规则依赖于注意力反馈和奖励预测错误来指导突触更新。我们证明该模型成功地获得了解决经典工作记忆任务的策略，例如延迟识别和延迟亲眼跳/反眼跳任务。此外，该模型解决了更复杂的任务，包括分层 12-AX 任务或 ABAB 有序识别任务，两者都需要一个代理在内存中独立存储和更新多个项目。此外，模型为这些任务获得的控制策略随后泛化到具有新刺激的新任务上下文，从而为神经网络架构带来符号产生式规则的品质。因此，WorkMATe 为灵活记忆控制的神经实现提供了一种新的解决方案。