The brain is targeted for processing temporal sequence information. It remains largely unclear how the brain learns to store and retrieve sequence memories. Here, we study how recurrent networks of binary neurons learn sequence attractors to store predefined pattern sequences and retrieve them robustly. We show that to store arbitrary pattern sequences, it is necessary for the network to include hidden neurons even though their role in displaying sequence memories is indirect. We develop a local learning algorithm to learn sequence attractors in the networks with hidden neurons. The algorithm is proven to converge and lead to sequence attractors. We demonstrate that the network model can store and retrieve sequences robustly on synthetic and real-world datasets. We hope that this study provides new insights in understanding sequence memory and temporal information processing in the brain.

翻译：大脑专门用于处理时序序列信息。目前尚不清楚大脑如何学习存储和检索序列记忆。本文研究二元神经元递归网络如何通过学习序列吸引子来存储预定义模式序列，并实现鲁棒性检索。研究表明，要存储任意模式序列，网络必须包含隐神经元——即使这些神经元在序列记忆表达中仅扮演间接角色。我们提出了一种局部学习算法，使含隐神经元的网络能够习得序列吸引子。该算法被证明具有收敛性，并能引导网络形成序列吸引子。实验证明，该网络模型可在合成数据集与真实世界数据集上稳健地存储和检索序列。希望本研究能为理解大脑序列记忆与时序信息处理机制提供新见解。