Lansner Laboratory, Department of Computational Science and Technology, Royal Institute of Technology, 10044 Stockholm, Sweden.
Lansner Laboratory, Department of Computational Science and Technology, Royal Institute of Technology, 10044 Stockholm, Sweden
eNeuro. 2020 Apr 23;7(2). doi: 10.1523/ENEURO.0374-19.2020. Print 2020 Mar/Apr.
Working memory (WM) is a key component of human memory and cognition. Computational models have been used to study the underlying neural mechanisms, but neglected the important role of short-term memory (STM) and long-term memory (LTM) interactions for WM. Here, we investigate these using a novel multiarea spiking neural network model of prefrontal cortex (PFC) and two parietotemporal cortical areas based on macaque data. We propose a WM indexing theory that explains how PFC could associate, maintain, and update multimodal LTM representations. Our simulations demonstrate how simultaneous, brief multimodal memory cues could build a temporary joint memory representation as an "index" in PFC by means of fast Hebbian synaptic plasticity. This index can then reactivate spontaneously and thereby also the associated LTM representations. Cueing one LTM item rapidly pattern completes the associated uncued item via PFC. The PFC-STM network updates flexibly as new stimuli arrive, thereby gradually overwriting older representations.
工作记忆 (WM) 是人类记忆和认知的关键组成部分。计算模型已被用于研究其潜在的神经机制，但忽略了短期记忆 (STM) 和长期记忆 (LTM) 相互作用对 WM 的重要作用。在这里，我们使用基于猕猴数据的新的前额叶皮层 (PFC) 和两个顶颞皮质区域的多区域尖峰神经网络模型来研究这些问题。我们提出了一种 WM 索引理论，解释了 PFC 如何关联、维持和更新多模态 LTM 表示。我们的模拟表明，通过快速海伯突触可塑性，同时短暂的多模态记忆线索如何在 PFC 中构建临时联合记忆表示作为“索引”。然后，该索引可以自发重新激活，从而也重新激活相关的 LTM 表示。通过 PFC 快速提示一个 LTM 项目可以快速完成相关的未提示项目。随着新刺激的到来，PFC-STM 网络会灵活更新，从而逐渐覆盖旧的表示。
