Wei Hui, Jin Xiao, Su Zihao
Laboratory of Cognitive Model and Algorithm, Department of Computer Science, Fudan University, No. 825 Zhangheng Road, Shanghai 201203, China.
Shanghai Key Laboratory of Data Science, No. 220 Handan Road, Shanghai 200433, China.
Brain Sci. 2022 Apr 26;12(5):547. doi: 10.3390/brainsci12050547.
Working memory (WM) plays an important role in cognitive activity. The WM system is used to temporarily store information in learning and decision-making. WM always functions in many aspects of daily life, such as the short-term memory of words, cell phone verification codes, and cell phone numbers. In young adults, studies have shown that a central memory store is limited to three to five meaningful items. Little is known about how WM functions at the microscopic neural level, but appropriate neural network computational models can help us gain a better understanding of it. In this study, we attempt to design a microscopic neural network model to explain the internal mechanism of WM. The performance of existing positive feedback models depends on the parameters of a synapse. We use a negative-derivative feedback mechanism to counteract the drift in persistent activity, making the hybrid positive and negative-derivative feedback (HPNF) model more robust to common disturbances. To fulfill the mechanism of WM at the neural circuit level, we construct two main neural networks based on the HPNF model: a memory-storage sub-network (the memory-storage sub-network is composed of several sets of neurons, so we call it "SET network", or "SET" for short) with positive feedback and negative-derivative feedback and a storage distribution network (SDN) designed by combining SET for memory item storage and memory updating. The SET network is a neural information self-sustaining mechanism, which is robust to common disturbances; the SDN constructs a storage distribution network at the neural circuit level; the experimental results show that our network can fulfill the storage, association, updating, and forgetting of information at the level of neural circuits, and it can work in different individuals with little change in parameters.
工作记忆（WM）在认知活动中起着重要作用。WM系统用于在学习和决策过程中临时存储信息。WM在日常生活的许多方面都发挥着作用，例如对单词、手机验证码和手机号码的短期记忆。在年轻人中，研究表明中央记忆存储限于三到五个有意义的项目。关于WM在微观神经层面如何发挥作用知之甚少，但合适的神经网络计算模型可以帮助我们更好地理解它。在本研究中，我们试图设计一个微观神经网络模型来解释WM的内部机制。现有的正反馈模型的性能取决于突触的参数。我们使用负导数反馈机制来抵消持续活动中的漂移，使混合正导数和负导数反馈（HPNF）模型对常见干扰更具鲁棒性。为了在神经回路层面实现WM的机制，我们基于HPNF模型构建了两个主要的神经网络：一个具有正反馈和负导数反馈的记忆存储子网（记忆存储子网由几组神经元组成，所以我们称它为“SET网络”，或简称为“SET”）和一个通过组合SET进行记忆项目存储和记忆更新而设计的存储分配网络（SDN）。SET网络是一种神经信息自我维持机制，对常见干扰具有鲁棒性；SDN在神经回路层面构建了一个存储分配网络；实验结果表明，我们的网络能够在神经回路层面实现信息的存储、关联、更新和遗忘，并且它可以在不同个体中工作，参数变化很小。
