A synaptic theory of Working Memory (WM) has been developed in the last decade as a possible alternative to the persistent spiking paradigm. In this context, we have developed a neural mass model able to reproduce exactly the dynamics of heterogeneous spiking neural networks encompassing realistic cellular mechanisms for short-term synaptic plasticity. This population model reproduces the macroscopic dynamics of the network in terms of the firing rate and the mean membrane potential. The latter quantity allows us to gain insight of the Local Field Potential and electroencephalographic signals measured during WM tasks to characterize the brain activity. More specifically synaptic facilitation and depression integrate each other to efficiently mimic WM operations via either synaptic reactivation or persistent activity. Memory access and loading are related to stimulus-locked transient oscillations followed by a steady-state activity in the β-γ band, thus resembling what is observed in the cortex during vibrotactile stimuli in humans and object recognition in monkeys. Memory juggling and competition emerge already by loading only two items. However more items can be stored in WM by considering neural architectures composed of multiple excitatory populations and a common inhibitory pool. Memory capacity depends strongly on the presentation rate of the items and it maximizes for an optimal frequency range. In particular we provide an analytic expression for the maximal memory capacity. Furthermore, the mean membrane potential turns out to be a suitable proxy to measure the memory load, analogously to event driven potentials in experiments on humans. Finally we show that the γ power increases with the number of loaded items, as reported in many experiments, while θ and β power reveal non monotonic behaviours. In particular, β and γ rhythms are crucially sustained by the inhibitory activity, while the θ rhythm is controlled by excitatory synapses.

在过去的十年中，已经出现了一种工作记忆（WM）的突触理论，它可以替代持久性峰值范式。在这种情况下，我们已经开发了一种神经质量模型，该模型能够精确地再现包含突触可塑性的现实细胞机制在内的异构尖峰神经网络的动力学。该种群模型就激发速率和平均膜电位而言，再现了网络的宏观动力学。后一个量使我们能够洞悉WM任务期间测得的局部场电位和脑电图信号，以表征大脑活动。更具体地说，突触的促进和抑制相互融合，以通过突触激活或持续活动有效地模仿WM操作。β - γ谱带，类似于人的触觉刺激和猴子的物体识别过程中在皮质中观察到的谱带。仅加载两个项目就已经出现了内存杂耍和竞争。但是，通过考虑由多个兴奋性种群和一个共同的抑制池组成的神经结构，可以在WM中存储更多物品。存储容量在很大程度上取决于项目的显示速度，并且在最佳频率范围内会最大化。特别是，我们提供了最大存储容量的解析表达式。此外，类似于在人类实验中事件驱动的电位，平均膜电位被证明是衡量记忆负荷的合适指标。最后，我们表明，γ如许多实验中所报道的，幂随装载项目的数量而增加，而θ和β幂则显示出非单调性。特别地，β和γ节律通过抑制活性至关重要地维持，而θ节律通过兴奋性突触来控制。