Sensory stimuli endow animals with the ability to generate an internal representation. This representation can be maintained for a certain duration in the absence of previously elicited inputs. The reliance on an internal representation rather than purely on the basis of external stimuli is a hallmark feature of higher-order functions such as working memory. Patterns of neural activity produced in response to sensory inputs can continue long after the disappearance of previous inputs. Experimental and theoretical studies have largely invested in understanding how animals faithfully maintain sensory representations during ongoing reverberations of neural activity. However, these studies have focused on preassigned protocols of stimulus presentation, leaving out by default the possibility of exploring how the content of working memory interacts with ongoing input streams. Here, we study working memory using a network of spiking neurons with dynamic synapses subject to short-term and long-term synaptic plasticity. The formal model is embodied in a physical robot as a companion approach under which neuronal activity is directly linked to motor output. The artificial agent is used as a methodological tool for studying the formation of working memory capacity. To this end, we devise a keyboard listening framework to delineate the context under which working memory content is (1) refined, (2) overwritten or (3) resisted by ongoing new input streams. Ultimately, this study takes a neurorobotic perspective to resurface the long-standing implication of working memory in flexible cognition.

感官刺激赋予动物产生内部表征的能力。在没有先前引出的输入的情况下，这种表示可以维持一定的持续时间。依赖内部表征而不是纯粹基于外部刺激是工作记忆等高阶功能的标志性特征。响应感官输入而产生的神经活动模式可以在先前的输入消失后很长时间内继续存在。实验和理论研究主要致力于了解动物如何在神经活动持续回响期间忠实地维持感官表征。然而，这些研究都集中在预先指定的刺激呈现协议上，默认情况下忽略了探索工作记忆内容如何与持续输入流相互作用的可能性。在这里，我们使用具有动态突触的尖峰神经元网络来研究工作记忆，该神经元具有短期和长期突触可塑性。形式模型体现在物理机器人中，作为一种伴随方法，其中神经元活动与运动输出直接相关。该人工智能体被用作研究工作记忆容量形成的方法论工具。为此，我们设计了一个键盘监听框架来描述工作记忆内容（1）精炼、（2）覆盖或（3）被持续的新输入流抵制的上下文。最终，这项研究从神经机器人的角度重新揭示了工作记忆对灵活认知的长期影响。