  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.
