Cognition involves using attended information, maintained in working memory (WM), to guide action. During a cognitive task, a correct response requires flexible, selective gating so that only the appropriate information flows from WM to downstream effectors that carry out the response. In this work, we used biophysically-detailed modeling to explore the hypothesis that network oscillations in prefrontal cortex (PFC), leveraging local inhibition, can independently gate responses to items in WM. The key role of local inhibition was to control the period between spike bursts in the outputs, and to produce an oscillatory response no matter whether the WM item was maintained in an asynchronous or oscillatory state. We found that the WM item that induced an oscillatory population response in the PFC output layer with the shortest period between spike bursts was most reliably propagated. The network resonant frequency (i.e., the input frequency that produces the largest response) of the output layer can be flexibly tuned by varying the excitability of deep layer principal cells. Our model suggests that experimentally-observed modulation of PFC beta-frequency (15–30 Hz) and gamma-frequency (30–80 Hz) oscillations could leverage network resonance and local inhibition to govern the flexible routing of signals in service to cognitive processes like gating outputs from working memory and the selection of rule-based actions. Importantly, we show for the first time that nonspecific changes in deep layer excitability can tune the output gate’s resonant frequency, enabling the specific selection of signals encoded by populations in asynchronous or fast oscillatory states. More generally, this represents a dynamic mechanism by which adjusting network excitability can govern the propagation of asynchronous and oscillatory signals throughout neocortex.

认知涉及使用保存在工作记忆 (WM) 中的参与信息来指导行动。在认知任务期间，正确的响应需要灵活的、选择性的门控，以便只有适当的信息从 WM 流向执行响应的下游效应器。在这项工作中，我们使用生物物理详细建模来探索假设，即前额叶皮层 (PFC) 中的网络振荡，利用局部抑制，可以独立地控制对 WM 中项目的反应。局部抑制的关键作用是控制输出尖峰脉冲之间的周期，并产生振荡响应，无论 WM 项目是保持在异步还是振荡状态。我们发现在 PFC 输出层中引起振荡群体响应的 WM 项目在尖峰爆发之间的周期最短，传播最可靠。输出层的网络谐振频率（即产生最大响应的输入频率）可以通过改变深层主细胞的兴奋性来灵活调整。我们的模型表明，实验观察到的 PFC β 频率（15-30 Hz）和伽马频率（30-80 Hz）振荡的调制可以利用网络共振和局部抑制来控制为认知过程服务的信号的灵活路由，例如门控工作记忆的输出和基于规则的动作的选择。重要的是，我们首次表明深层兴奋性的非特异性变化可以调节输出门的谐振频率，能够特定选择由处于异步或快速振荡状态的种群编码的信号。更一般地说，这代表了一种动态机制，通过该机制调整网络兴奋性可以控制整个新皮层的异步和振荡信号的传播。