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.

大脑针对处理时间序列信息，如何学习存储和检索序列记忆仍不清楚。本研究探讨了二元神经元回归网络如何学习序列吸引子以存储预定义的模式序列，并能够强大地检索它们。通过发展一种本地学习算法，证明了该算法能够使带有隐藏神经元的网络学习序列吸引子，且能够在合成和实际数据集上有效地存储和检索序列。希望本研究为理解大脑中的序列记忆和时间信息处理提供新的见解。